Blood Pressure Signals Research Articles

Clinical Validation of Non-invasive Simulation-Based Determination of Vascular Impedance, Wave Intensity, and Hydraulic Work in Patients Undergoing Transcatheter Aortic Valve Replacement.

The impact of Aortic Stenosis (AS) on the left ventricle (LV) extends beyond the influence of the pressure drop across the stenotic valve, but also includes the additional serial afterload imposed by the vascular system. Aortic input impedance is the gold standard for comprehensively studying the contribution of the vascular system to total myocardial afterload, but in the past measurement has been challenging arising from the need for invasive catheterization or specialized equipment to precisely record time-resolved blood pressure and flow signals. The goal of this work was to develop and validate a novel simulation-based method for determining aortic input impedance using only clinically available echocardiographic data and a simple blood pressure measurement. A simulation-based method to determine vascular impedance was developed using echocardiographic data and a brachial blood pressure measurement. Simulation-based impedance was compared to impedance calculated from echocardiographic flow data and pressure data from a non-invasive central pressure measurement device. In validation analysis comparing patient-specific simulation-based vascular impedance to non-invasively measured impedance, correlation between methods across a range of vascular parameters varied between R2=0.40 and 0.99. A tendency was seen toward underestimation of pressure waveforms in point-by-point comparison of measured and simulated waveforms with an overall mean difference of 4.01mmHg. Requiring only non-invasive clinical data that are widely available, simulation-based vascular impedance has the potential to allow for easier, more widespread, and larger-scale investigation of the effect of vascular impedance on total LV afterload.

Heart Rate Variability and Cerebral Autoregulation in Patients with Traumatic Brain Injury with Paroxysmal Sympathetic Hyperactivity Syndrome.

Severe traumatic brain injury (TBI) can lead to transient changes in autonomic nervous system (ANS) functioning and development of paroxysmal sympathetic hyperactivity (PSH) syndrome. Clinical manifestation of ANS disorders may be obscured by therapeutic interventions in TBI. This study aims to analyze ANS metrics and cerebral autoregulation in patients with PSH syndrome to determine their significance in early prognostication. This single-center retrospective study investigated the relationship between changes in ANS metrics, cerebral autoregulation, and PSH syndrome. Arterial blood pressure and intracranial pressure signals were monitored for 5 days post TBI. ANS metrics included time and frequency domain heart rate variability (HRV) metrics. Cerebral autoregulation was assessed using the pressure reactivity index. Sixty-six patients with severe TBI (median age 33 [interquartile range 26-50] years) were analyzed, and PSH was confirmed in nine cases. Impairment of cerebral autoregulation was observed in 67% of patients with PSH and 72% without the syndrome. Patients with PSH had higher HRV in the low-frequency range (LF; 253 ± 178 vs. 176 ± 227 ms2; p = 0.035) and lower heart rates (HRs; 70 ± 7 vs. 78 ± 19 bpm; p = 0.027) compared to those without PSH. A receiver operating characteristic curve analysis indicated that HR (area under the curve(AUC) = 0.73, p = 0.006) and HRV in the LF (AUC = 0.70, p = 0.009) are moderate predictors of PSH. In the multiple logistic regression model for PSH, diffuse axonal trauma (odds ratio(OR) = 10.82, 95% confidence interval(CI) = 1.70-68.98, p = 0.012) and HR (OR =0.91, 95% CI 0.84-0.98, p = 0.021) were significant factors. Elevated HRV in the LF and decreased HR may serve as early predictors of PSH syndrome development, particularly in patients with diffuse axonal trauma. Further research is needed to investigate the utility of the cerebral autoregulation-ANS relationship in PSH prognostication.

Understanding perioperative risk determinants in carotid endarterectomy: the impact of compromised circle of Willis morphology on inter-hemispheric blood flow indices based on intraoperative internal carotid artery stump pulse pressure and backflow patterns.

Carotid artery stenosis (CAS) often requires surgical intervention through carotid endarterectomy (CEA) to prevent stroke. Accurate cerebrovascular risk assessments are crucial in CEA, as poor collateral circulation can lead to insufficient interhemispheric blood flow compensation, resulting in ischemic complications. Therefore, understanding perioperative risk determinants is vital. This study aims to determine the impact of compromised circle of Willis (CoW) morphology on inter-hemispheric blood flow, focusing on indices based on intraoperative internal carotid artery stump pulse pressure and backflow patterns. In 80 CAS patients who underwent CEA, preoperative CT angiography for CoW was conducted. Patients were categorized into five subgroups based on their CoW anatomy and three additional groups based on intraoperative internal carotid artery (ICA) stump backflow patterns evaluated by the surgeon. Continuous blood pressure signals, including systolic, diastolic, mean, and pulse pressure values, were recorded during the procedure. The relationship between CoW anatomical variants and the systolic and diastolic segments of the averaged pressure waveforms, particularly diastolic pressure decay, was analyzed. The correlation between CoW anatomy and stump backflow intensity was also examined. Significant variability in ICA stump backflow and pressure values was evident across CoW variants. Patients with compromised CoW morphology exhibited weaker backflow patterns and lower ICA stump pulse pressure values, consistent with impaired interhemispheric blood flow. Notably, ICA stump diastolic pressure decay was consistent across most CoW variant groups, indicating developed collateral circulation in cases with CoW anomalies. Thus, impaired CoW integrity is associated with compromised interhemispheric blood flow indices based on intraoperative ICA stump pulse pressure and backflow patterns during CEA. Integrating intraoperative pulse waveform analysis with preoperative CT angiography provides a more detailed assessment of cerebrovascular risk, guiding the selective use of shunts. This combined approach may improve surgical outcomes and patient safety by identifying patients at increased risk of perioperative neurological events due to CoW anomalies.

Analysis of Physiological Signals to Detect the Symptoms of Paralysis Disorder using Neural Network Approach: a Cohort Study

As per World Medical Associations, Paralysis attack is one of the leading causes of disorder in the human subjects after other chronic disorders such as Cancer and Heart Attack. Due to the attack of Paralysis, the body parts of human body become un responsive due to which the person may be physically disabled from hands or legs-based locomotion. There is scarcity in the methodologies who can early indicate the symptoms and alert the medical practitioners or human subject under surveillance. In this study, the health-related signals were studied and as per consultation of medical experts, total eight key parameters were identified and then based on that the output decision that weather person has symptoms of paralysis or not. The observatory data base has been prepared from more than 40,000 human subjects for the duration January 2018 to December 2023. As per categorical analysis based on training, validation and testing of three-layer Recurrent Neural Network, it has been observed that the ECG, Blood pressure, Heart Rate and PPG signals are key indicators to judge the susceptance of Paralysis disorder in the human subjects. Based on the evaluation parameters of machine learning models, the conjunction of response in association with selected input parameters gives 94% accuracy than other models. Our study helps research community to develop such methodologies by which the early indication of such disorders can be helpful to human health.

A Real-Valued Kalman Estimation Method for Harmonic Signal Analysis in Biomedical Applications

This work presents a methodology for obtaining the harmonic estimation of biomedical signals such as electrocardiogram, cardiorespiratory and blood pressure signals. The proposed methodology is achieved using polynomial approximation and the Kalman filter. As advantage, the technique includes instant estimations of signal harmonics and its derivatives using a real-valued model. Furthermore, a comparison of the results is conducted with the Savitzky-Gola, nonlinear tracking differentiator methods, extended state observer and digital differentiator base on Taylor series. The results suggest that the proposed method has the potential to enhance the quality of signal measurements, especially in the presence of noise.

Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment

Objective.We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.Approach.Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.Main results.Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.Significance.The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.

Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography Signals Using a Deep Learning Model: Preliminary Study.

Beat-by-beat monitoring of hemodynamic parameters in the left ventricle contributes to the early diagnosis and treatment of heart failure, valvular heart disease, and other cardiovascular diseases. Current accurate measurement methods for ventricular hemodynamic parameters are inconvenient for monitoring hemodynamic indexes in daily life. The objective of this study is to propose a method for estimating intraventricular hemodynamic parameters in a beat-to-beat style based on non-invasive PCG (phonocardiogram) and PPG (photoplethysmography) signals. Three beagle dogs were used as subjects. PCG, PPG, electrocardiogram (ECG), and invasive blood pressure signals in the left ventricle were synchronously collected while epinephrine medicine was injected into the veins to produce hemodynamic variations. Various doses of epinephrine were used to produce hemodynamic variations. A total of 40 records (over 12,000 cardiac cycles) were obtained. A deep neural network was built to simultaneously estimate four hemodynamic parameters of one cardiac cycle by inputting the PCGs and PPGs of the cardiac cycle. The outputs of the network were four hemodynamic parameters: left ventricular systolic blood pressure (SBP), left ventricular diastolic blood pressure (DBP), maximum rate of left ventricular pressure rise (MRR), and maximum rate of left ventricular pressure decline (MRD). The model built in this study consisted of a residual convolutional module and a bidirectional recurrent neural network module which learnt the local features and context relations, respectively. The training mode of the network followed a regression model, and the loss function was set as mean square error. When the network was trained and tested on one subject using a five-fold validation scheme, the performances were very good. The average correlation coefficients (CCs) between the estimated values and measured values were generally greater than 0.90 for SBP, DBP, MRR, and MRD. However, when the network was trained with one subject's data and tested with another subject's data, the performance degraded somewhat. The average CCs reduced from over 0.9 to 0.7 for SBP, DBP, and MRD; however, MRR had higher consistency, with the average CC reducing from over 0.9 to about 0.85 only. The generalizability across subjects could be improved if individual differences were considered. The performance indicates the possibility that hemodynamic parameters could be estimated by PCG and PPG signals collected on the body surface. With the rapid development of wearable devices, it has up-and-coming applications for self-monitoring in home healthcare environments.

Detection of contractility changes in the heart from arterial blood pressure data using symmetric Projection Attractor Reconstruction

The potential for unintended drug induced changes in cardiac contractility is a major concern in medicines development. Whilst direct left ventricular pressure (LVP) measurement is the gold standard for measuring cardiac contractility in vivo, it is resource intensive and poses a welfare burden on research animals. In contrast, arterial blood pressure (BP) measurement has fewer challenges. Symmetric Projection Attractor Reconstruction (SPAR) is a signal processing technique which transforms physiological time-series signals into a corresponding visual image (‘attractor’), amplifying morphology changes within physiological waveforms. It was hypothesized that SPAR analysis of BP signals would provide a surrogate measure of cardiac contractility by specifically amplifying the maximum slope of the systolic upstroke. BP (abdominal aorta) signals obtained from beagle dogs, treated with positive and negative inotropes, were retrospectively analysed to identify signal features that correlated with the maximum upslope of the LVP signal from simultaneously acquired LVP recordings. SPAR transformation of BP signals quantified drug induced changes in the maximum slope of the systolic upstroke. We identified key SPAR metrics that provided >0.8 correlation with the LVP maximum upslope, outperforming the BP systolic upstroke alone. This was observed for all 4 different drugs, doses and time points evaluated across studies. Thus, we conclude that the SPAR measures derived from the BP signal could be used as a first pass in vivo screen to flag any risk of drug induced changes in cardiac contractility during the conduct of non-clinical medicines development, potentially reducing or replacing the need to perform direct left ventricular measurements.

ARTERIAL BLOOD PRESSURE WAVEFORM ARTIFACTS DETECTION USING SHORT-TIME FOURIER TRANSFORM

High-frequency waveform recordings of biological signals enable more detailed data analysis and deeper physiological exploration. However, the waveform data—like invasive arterial blood pressure (ABP)—are particularly susceptible to frequent contamination with artifacts that can devalue the subsequent calculations like pressure reactivity index (PRx). This study aimed to verify the ability of the short-time Fourier transform (STFT) based algorithm to detect artifacts in the ABP waveform. Four types of modeled artifacts (rectangular, fast impulse, sawtooth and baseline drift) with different durations and amplitudes were inserted into undisturbed ABP waveforms. Short-time Fourier transform with a 5-second time window is computed on artifact-polluted ABP signals to detect changes in the frequency domain caused by these artifacts. An algorithm with three decision-making rules based on the dominant frequency component, standardized power spectrum, and the value of the second harmonic of the dominant frequency was used. Only segments that passed all three rules were labeled as artifact-free. Results indicated high sensitivity (93.35% and 94.83%) in detecting rectangular and sawtooth artifacts, with specificity exceeding 99% for both. Baseline drift artifact was detected with a low sensitivity of 5.02%, and fast impulse was not detected. This study proposes the application of a short-time Fourier transform-based algorithm to enhance the detection of clinically significant artifacts in arterial blood pressure signals, particularly relevant for PRx and other secondary calculations.

Stress Detection Based on Naïve Bayes Algorithm

Tension is a widespread concern impacting the mental and physical health of individuals, resulting in various health issues. Machine-based learning (ML) has emerged as a favored tool for identifying tension. ML strategies have displayed encouraging outcomes in recognizing trends and attributes from different physiological and behavioral data streams such as heart rate, blood pressure, and vocal signals. The main objective of utilizing ML for tension detection is to offer precise, non-invasive, and economical approaches for early tension identification and intervention. Overall, ML-based tension detection shows substantial potential for delivering an impartial, effective, and expandable strategy for tension monitoring and intervention. Additional exploration is needed to tackle the obstacles linked to data compilation, attribute extraction, and model adaptation to varied populations and scenarios. Keywords: Automated Learning, Simple-minded Bayes, Information, Gloom, Tension.

Novel Metric for Non-Invasive Beat-to-Beat Blood Pressure Measurements Demonstrates Physiological Blood Pressure Fluctuations during Pregnancy.

Beat-to-beat (B2B) variability in biomedical signals has been shown to have high diagnostic power in the treatment of various cardiovascular and autonomic disorders. In recent years, new techniques and devices have been developed to enable non-invasive blood pressure (BP) measurements. In this work, we aim to establish the concept of two-dimensional signal warping, an approved method from ECG signal processing, for non-invasive continuous BP signals. To this end, we introduce a novel BP-specific beat annotation algorithm and a B2B-BP fluctuation (B2B-BPF) metric novel for BP measurements that considers the entire BP waveform. In addition to careful validation with synthetic data, we applied the generated analysis pipeline to non-invasive continuous BP signals of 44 healthy pregnant women (30.9 ± 5.7 years) between the 21st and 30th week of gestation (WOG). In line with established variability metrics, a significant increase (p < 0.05) in B2B-BPF can be observed with advancing WOGs. Our processing pipeline enables robust extraction of B2B-BPF, demonstrates the influence of various factors such as increasing WOG or exercise on blood pressure during pregnancy, and indicates the potential of novel non-invasive biosignal sensing techniques in diagnostics. The results represent B2B-BP changes in healthy pregnant women and allow for future comparison with those signals acquired from women with hypertensive disorders.

Enhanced Cardiovascular Assessment in Rats Using a Wireless Data Logger

Study objectives and hypothesis: Cardiovascular assessment in animals can be complicated by signal loss of real-time telemetry. We evaluated the benefits of using a wireless data logger to capture 18 hours of blood pressure (BP) and electrocardiogram (ECG) data. This approach eliminates data loss, simplifies analysis and improves results. Methods: One male Sprague Dawley rat was implanted with a PBTA-L implant (Stellar, TSE Systems) using established procedures to collect BP and ECG. All procedures were approved by the AmplifyBio IACUC. Memory and real-time (RT) BP and ECG were recorded at 500Hz for 18+ hours in the acquisition software (Notocord-hem, Instem) on separate days. ECG RR intervals and BP intervals were compared for memory versus RT recordings. Time-based (RMSSD, SDNN, SDANN) and frequency-based (LF, HF) HRV assessment was performed in 5-minute increments. HRV from ECG vs BP were compared for the memory recording. Data and Results: High quality BP and ECG waveforms were recorded to memory and retrieved into the software with no manual intervention or loss of data. The RT recording included 0.48% signal loss. Interval outliers were removed for HRV assessment using thresholds to limit valid RR/beat intervals. This worked well for BP but was less effective for ECG as there were more erroneous intervals due to movement artifact. Time- and frequency-based HRV parameters were compared for memory vs RT recordings for BP and ECG intervals. HRV from BP showed lower variability for RT vs memory (LF↓24.3%, HR↓6.3%, RMSSD↓0.1%, SDNN↓15.4%, SDANN↓6.1%), while HRV from ECG generally produced higher variability for RT vs memory (LF↑111.8%, HF↑175.1%, RMSSD↑93.9%, SDNN↑16.7%, SDANN↓9.5%). These findings can be explained as follows: BP intervals produced less variability with the RT recording because the long intervals caused by signal loss are distinct and consistently removed by threshold criteria. The lost intervals result in lower HRV. ECG intervals produced more variability with the RT recording because the recordings were 1 week apart, and the later RT recording had more movement artifact on the ECG resulting in inaccurate QRS detection. The erroneous intervals were not as effectively removed by thresholds, resulting in higher HRV. We also compared HRV from the BP and ECG signals in the memory recording. The LF and HF powers were 5.4 and 4.5 times as high respectively for the RR (ECG) vs beat (BP) intervals. The time-based HRV parameters for RMSSD and SDNN were higher by 84% and 14%, while SDANN was very similar with ECG results 2% less than BP results. Conclusions: HRV analysis highlights the benefits of using a wireless data logger to record cardiovascular data. Our assessment demonstrated improvements in the effciency and accuracy of HRV analysis. As a secondary outcome, we demonstrated potential benefits of performing HRV analysis on BP vs ECG signals. None. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Artificial tactile system for pressure monitoring in extracorporeal circulation processes

Current intraoperative pressure monitoring methods still face significant limitations in perception and feedback, struggling to strike a balance between precision and wearable flexibility. Inspired by biological skin, we propose a biomimetic tactile sensing system for pressure monitoring during extracorporeal circulation, comprising flexible pressure sensors and artificial synaptic transistors. Aimed at addressing the aforementioned issues, our system employs a pyramid-shaped elastic design for flexible pressure sensors, utilizing biocompatible materials polydimethylsiloxane and multi-walled carbon nanotubes as the strain-sensitive layer. This configuration boasts ultra-high sensitivity and resolution (115 kPa−1), accurately detecting subtle pressure changes, such as blood circulation wall pressures. With artificial synaptic transistors as the information processing core, our system successfully simulates crucial neural processing functions, including excitatory post-synaptic currents and double-pulse facilitation, while providing alerts for abnormal blood pressure signals. This system facilitates real-time data processing at the device edge, reducing power consumption, improving efficiency, and better addressing the demands of large-scale physiological pressure data processing. It presents a significant reference for future developments in biomedical electronics and bionics.

Exploring the potential effect of electroacupuncture on cardiovascular function and lipid profiles in spontaneously hypertensive rats

BackgroundInvestigating the effects of electroacupuncture (EA) treatment on cardiovascular function and aortic lipid profiles in spontaneously hypertensive rats (SHR) constitutes the foundational focus of this study. The overarching goal is to comprehensively elucidate the alterations brought about by EA treatment and to assess its potential as an alternative therapy for hypertension. MethodsConsecutive EA treatments were administered to SHR, and the effects on systolic blood pressure, cardiac function, and hypertension-related neuronal signals were assessed. Aortic lipid profiles in vehicle-treated SHR and EA-treated SHR groups were analyzed using mass spectrometry-based lipid profiling. Additionally, the expression of Cers2 and GNPAT, enzymes involved in the synthesis of specific aortic lipids, was examined. ResultsThe study demonstrated that consecutive EA treatments restored systolic blood pressure, improved cardiovascular function, and normalized hypertension-related neuronal signals in SHR. Analysis of the aortic lipid profiles revealed distinct differences between the vehicle-treated SHR group and the EA-treated SHR group. Specifically, EA treatment significantly altered the levels of aortic sphingomyelin and phospholipids, including very long-chain fatty acyl-ceramides and ether phosphatidylcholines. These changes in aortic lipid profiles correlated significantly with systolic blood pressure and cardiac function indicators. Furthermore, EA treatment significantly altered the expression of Cers2 and GNPAT. ConclusionsThe findings suggest that EA may influence cardiovascular functions and aortic lipid profiles in SHR.

Autonomic Nervous System Activity and Dental Anxiety in the Northern Finland Birth Cohort (NFBC1966) Population.

Autonomic nervous system (ANS) activity may be associated with dental anxiety; however, no studies exist on the topic. The aim of this study was to assess if ANS activity and dental anxiety are associated. Data were collected as part of a Northern Finland Birth Cohort Study, NFBC1966, at the age of 46 years with eligible data on 1905 participants. Dental anxiety was measured using the Modified Dental Anxiety Scale (MDAS) categorized as follows: 19-25 = high, 10-18 = moderate, 5-9 = no to low dental anxiety. Heart rate variability (HRV) data were collected using an HR monitor and a standard lead-II electrocardiogram (ECG). Baroreflex sensitivity (BRS) was calculated from continuous ECG and blood pressure signals. Participants were categorized into three groups as follows: Low = the lowest 20th percentile, Mid = medium 21-79th percentile, and High = the highest 20th percentile according to their ANS variables. The associations between the MDAS and ANS activity parameters were evaluated using unordered multinomial logit models adjusted for comorbidities, β-blockers, BMI, smoking, and education. High heart rate, reduced HRV, low cardiac vagal modulation, and poor BRS were associated with moderate dental anxiety, and high cardiac vagal modulation and good BRS were associated with no to low dental anxiety. Poorer ANS activity might predispose some to dental anxiety, and better ANS activity might protect from dental anxiety.

Secure medical sensor monitoring framework using novel hybrid encryption algorithm driven by internet of things

The Internet of Things (IoT) makes intelligent identification and management possible by enabling all items to connect to the Internet and exchange data through various new technologies. In recent years, it has been noted that life expectations throughout the globe have gradually increased, which has caused the population of older people to often grow. An unstable environment has been created by a rise in the population of seniors, understaffing in medical facilities, and increased government spending on healthcare. This environment could ultimately result in the financial collapse of government healthcare programs and lower standards of care for all people. It is challenging for hospitals to care for all of the patients since patient numbers have virtually doubled every four decades. A significant societal concern is ensuring that patients have a decent quality of life. To continuously monitor a patient’s physiological characteristics using sensors on the patient’s body, healthcare monitoring systems are increasingly important nowadays. This technology can continuously check physiological data every 10 s. Nodes with sensors may measure things like blood pressure, body temperature, heart rate, and ECG signals. Let’s use some pre-processing methods once the sensor data has been collected to eliminate duplicates, mistakes (outliers), and missing values. With the support of a creative technique, this healthcare monitoring system has enabled safe IoT-based communication. The triple protection of improved AES, modified RSA, and revised Paillier encryption would raise the bar for security measures.

False alarm detection in intensive care unit for monitoring arrhythmia condition using bio-signals

PurposeA novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.Design/methodology/approachThree machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.FindingsThe proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.Originality/valueAs ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

Sport-related concussion alters cerebral hemodynamic activity during controlled respiration.

Sport-related concussion (SRC) is known to disrupt neurohemodynamic activity, cardiac function, and blood pressure (BP) autoregulation. This study aims to observe changes in cerebrovascular and cardiovascular responses during controlled respiration after sustaining an SRC. University varsity athletes (n = 81) completed a preseason physiological assessment and were followed up within 5 days of sustaining an SRC. During preseason and follow-up assessments, participants' continuous beat-to-beat BP was collected by finger photoplethysmography, and right prefrontal cortex oxygenation was collected using near-infrared spectroscopy (NIRS). Participants completed 5 min of seated rest and 5 min of a 6-breaths per minute controlled breathing protocol (5 s inhale and 5 s exhale; 0.10 Hz). Wavelet transformation was applied to the NIRS and BP signals, separating them into respiratory (0.10-0.6 Hz) and cardiac (0.6-2 Hz) frequency intervals. Of the 81 participants, 74 had a usable BP signal, 43 had usable NIRS signals, and 28 had both usable BP and NIRS signals. Wavelet amplitudes were calculated and coherence between NIRS and BP on the 28 participants were assessed. There was a significant (P < 0.05) decrease in oxygenated hemoglobin amplitude from 0.062 to 0.054 Hz and hemoglobin difference amplitude from 0.059 to 0.051 Hz, both at the respiratory (0.10-0.6 Hz) frequency interval, from preseason to acute SRC, respectively. Therefore, during controlled respiration, there was a reduction in intensity at the respiratory band, suggesting a protective, reduced respiratory contribution to cerebral hemodynamic activity following acute SRC.NEW & NOTEWORTHY This study investigated cerebral hemodynamic activity following sport-related concussion. Prefrontal cortex oxygenation was assessed by near-infrared spectroscopy (NIRS) during a controlled breathing protocol. Wavelet transformation of the NIRS signals showed significant decreases in HbO2 and HbD amplitude at the respiratory frequency interval (0.10-0.6 HZ) from preseason baseline to acute concussion. These results suggest a decreased respiratory contribution to cerebral hemodynamic activity following acute concussion.

CVRanalysis: a free software for analyzing cardiac, vascular and respiratory interactions.

Introduction: Simultaneous beat-to-beat R-R intervals, blood pressure and respiration signals are routinely analyzed for the evaluation of autonomic cardiovascular and cardiorespiratory regulations for research or clinical purposes. The more recognized analyses are i) heart rate variability and cardiac coherence, which provides an evaluation of autonomic nervous system activity and more particularly parasympathetic and sympathetic autonomic arms; ii) blood pressure variability which is mainly linked to sympathetic modulation and myogenic vascular function; iii) baroreflex sensitivity; iv) time-frequency analyses to identify fast modifications of autonomic activity; and more recently, v) time and frequency domain Granger causality analyses were introduced for assessing bidirectional causal links between each considered signal, thus allowing the scrutiny of many physiological regulatory mechanisms. Methods: These analyses are commonly applied in various populations and conditions, including mortality and morbidity predictions, cardiac and respiratory rehabilitation, training and overtraining, diabetes, autonomic status of newborns, anesthesia, or neurophysiological studies. Results: We developed CVRanalysis, a free software to analyze cardiac, vascular and respiratory interactions, with a friendly graphical interface designed to meet laboratory requirements. The main strength of CVRanalysis resides in its wide scope of applications: recordings can arise from beat-to-beat preprocessed data (R-R, systolic, diastolic and mean blood pressure, respiration) or raw data (ECG, continuous blood pressure and respiratory waveforms). It has several tools for beat detection and correction, as well as setting of specific areas or events. In addition to the wide possibility of analyses cited above, the interface is also designed for easy study of large cohorts, including batch mode signal processing to avoid running repetitive operations. Results are displayed as figures or saved in text files that are easily employable in statistical softwares. Conclusion: CVRanalysis is freely available at this website: anslabtools.univ-st-etienne.fr. It has been developed using MATLAB® and works on Windows 64-bit operating systems. The software is a standalone application avoiding to have programming skills and to install MATLAB. The aims of this paper area are to describe the physiological, research and clinical contexts of CVRanalysis, to introduce the methodological approach of the different techniques used, and to show an overview of the software with the aid of screenshots.

Malignant arrhythmias detection using a synthesis-by-analysis modeling method of arterial blood pressure signal

Extreme bradycardia, extreme tachycardia, ventricular flutter fib, and ventricular tachycardia are four malignant arrhythmias (MAs) that lead to sudden cardiac death. It is very important to detect them in daily life. The arterial blood pressure (ABP) signal contains abundant pathological information about four MAs and is easy to be recorded under domestic conditions. Thus, a synthesis-by-analysis (SA) modeling method for ABP signal was proposed to detect the four MAs in this study. The average models of MAs and healthy subjects were obtained by SA modeling, and the change of each ABP wave was quantitively described by twelve parameters of wave models. Then, the probabilistic neural network (PNN) and random forest (RF) are trained to detect the MAs. The experimental data were employed from Fantasia and the 2015 PhysioNet/CinC Challenge databases. The SA modeling results show that some pathological and physiological changes could be extracted from the average models. The two-sample ks-test results between different groups are markedly different (h = 1, p < 0.05). The detection results show that the performances of PPN classifiers are less than that of RF. The kappa coefficients (KC) for the RF classifiers are 97.167 ± 1.46 %, 97.888 ± 0.808 %, 99.895 ± 0.545 %, 98.575 ± 1.683 % and 92.241 ± 1.517 %, respectively. The mean KC is 97.083 ± 0.67 %. Compared to the performance of some existing studies, the proposed method has better performance and is potential to diagnose MAs in m-health.