Signal Quality Index Research Articles

Point-of-care screening for heart failure with reduced ejection fraction using simple pulse oximetry

Abstract Background More than 64 million patients with heart failure (HF) are burdened with poor quality of life, frequent hospitalizations, and high readmission rates. While HF with reduced ejection fraction (HFrEF) accounts for approximately half of these patients, there is currently no effective tool for performing cost-effective and reliable early-stage screening. For this purpose, we derived and cross-validated a machine learning (ML) based system capable of identifying HFrEF based on analysis of the photoplethysmographic (PPG) signal recorded by a simple pulse-oximeter. Methods Patients attending a routine ambulatory check-up at an outpatient clinic were included in the study. Left ventricular ejection fraction was determined by standard transthoracic echocardiography. Diagnosis of HF was based on the latest ESC recommendations for HF. PPG was measured using a standard oximeter modified to record raw PPG signal. Before processing, the quality of the measured PPG signal was assessed by signal quality indices to determine whether the quality of signal was sufficient for further analysis. In brief, the signal processing involved noise removal, filtering for specific frequencies, automatic artifact detection and identification of individual heartbeats, on which specific parts of the signal and signal characteristics, necessary for further analysis, were identified. Four different ML processing pipelines with different approaches to feature selection and class balancing were deployed, validated using the K-fold cross-validation and compared. Results A total of 371 patients were included in the study: 54 patients with HFrEF (mean age 66.87 ± 14.35, 74.07% male) and 317 patients with HFmrEF (4), HFpEF (42) or no history of HF (271) (mean age 54.12 ± 17.79, 51.73% male). From the 4 ML pipelines, LightGBM with SMOTE + Edited Nearest Neighbors balancing had the highest cross-validated predictive performance for detection of HFrEF (c-statistics 0.8, CI 0.69 – 0.89). Conclusions Our results suggest that HFrEF can be effectively screened using a simple pulse oximeter, thanks to ML signal analysis. This solution could be adapted to a simple, non-invasive, readily available, time-saving, and inexpensive point-of-care diagnostic tool for primary care. Early disease detection and referral to a cardiologist (or HF specialist) would have a positive impact on patient morbidity and mortality.Receiver Operating Characteristics CurveModel Performance Metrics

Accuracy enhancement of metabolic index-based blood glucose estimation with a screening process for low-quality data.

Many researchers have proposed various non-invasive glucose monitoring (NIGM) approaches using wearable or portable devices. However, due to the limited capacity of detectors for such compact devices and the movement of the body during measurement, the precision of the acquired data frequently diminishes, which can cause problems during actual use in daily life. In addition, intensive smoothing is often used in post-processing to mitigate the effects of erroneous values. However, this requires a considerable amount of data and results in a delay in the response to the actual blood glucose level (BGL). Instead of just applying data smoothing in the post-process of the data acquisition, we propose an active low-quality data screening method in the pre-process. In the proposal phase of the screening process, we employ an analytical approach to examine and formulate factors that might affect the BGL estimation accuracy. A signal quality index inspired by the standard deviation concept is introduced to detect visually apparent noise on signals. Furthermore, the total estimation error in the metabolic index (MI) is calculated based on potential perturbations defined by the signal-to-noise ratio (SNR) and the uncertainty due to discrete sampling. Thereafter, the acquired data were screened by these quality indices. By applying the proposed data screening process to the data obtained from a commercially available smartwatch device in the pre-process, the estimation accuracy of the MI-based BGL was improved significantly. Adopting the proposed screen process improves BGL estimation accuracy in the smartwatch-based prototype. Applying the proposed screen process will facilitate the integration of wearable and continuous BGL monitoring into size- and SNR-limited devices such as smartwatches and smart rings.

Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing.

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts.

Quality of Electrocardiography Recorded with Consumer Smart Shirts.

Smart wearables support continuous monitoring of vital signs for early detection of deteriorating health. However, the devices and sensors require sufficient quality to produce meaningful signals, in particular, if data is acquired in motion. In this study, we equipped 48 subjects with smart shirts recording one-lead electrocardiography (ECG), thoracic and abdominal respiratory inductance plethysmography, and three-axis acceleration. For 10 min each, the subjects sit, stand, walk, and run, with a resting period of 5 min in between each activity. We preprocessed the electrocardiogram and applied a signal quality index. We analyzed the signal quality index grouped by the activity and participants. For sitting, standing, walking, and running, the ECG signals provide acceptable quality over 73.20 %, 91.85 %, 12.26 %, and 13.14 % of the recording time. In conclusion, smart wearables may be useful for continuous health monitoring of people with a sedentary lifestyle, but rather not for sportive activities.

SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia.


Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, accurate and real-time estimation of DOA remains a challenging task. In this paper, we proposed a signal quality index (SQI) network (SQINet) for assessing the electroencephalogram (EEG) signal quality and a DOA network (DOANet) for analyzing EEG signals to precisely estimate DOA. The two networks are termed SQI-DOANet.
Approach:
The SQINet contained a shallow convolutional neural network to quickly determine the quality of the EEG signal. The DOANet comprised a feature extraction module for extracting features, a dual attention module for fusing multi-channel and multi-scale information, and a gated multilayer perceptron module for extracting temporal information. The performance of the SQI-DOANet model was validated by training and testing the model on the large VitalDB database, with the bispectral index (BIS) as the reference standard.
Main results:
The proposed DOANet yielded a Pearson correlation coefficient with the BIS score of 0.88 in the 5-fold cross-validation, with a mean absolute error (MAE) of 4.81. The mean Pearson correlation coefficient of SQI-DOANet with the BIS score in the 5-fold cross-validation was 0.82, with an MAE of 5.66.
Significance:
The SQI-DOANet model outperformed three compared methods. The proposed SQI-DOANet may be used as a new deep learning method for DOA estimation. The code of the SQI-DOANet will be made available publicly at https://github.com/YuRui8879/SQI-DOANet.&#xD.

From sleep patterns to heart rhythm: Predicting atrial fibrillation from overnight polysomnograms

BackgroundAtrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Given the prevalence of obstructive sleep apnea among AF patients, electrocardiogram (ECG) analysis from polysomnography (PSG) offers a unique opportunity for early AF prediction. Our aim is to identify individuals at high risk of AF development from single‑lead ECGs during standard PSG. MethodsWe analyzed 18,782 single‑lead ECG recordings from 13,609 subjects undergoing PSG at the Massachusetts General Hospital sleep laboratory. AF presence was identified using ICD-9/10 codes. The dataset included 15,913 recordings without AF history and 2054 recordings from patients diagnosed with AF between one month to fifteen years post-PSG. Data were partitioned into training, validation, and test cohorts ensuring that individual patients remained exclusive to each cohort. The test set was held out during the training process.We employed two different methods for feature extraction to build a final model for AF prediction: Extraction of hand-crafted ECG features and a deep learning method. For extraction of ECG-hand-crafted features, recordings were split into 30-s windows, and those with a signal quality index (SQI) below 0.95 were discarded. From each remaining window, 150 features were extracted from the time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1800 features (12 × 150).A pre-trained deep neural network from the PhysioNet Challenge 2021 was updated using transfer learning to discriminate recordings with and without AF. The model processed PSG ECGs in 16-s windows to generate AF probabilities, from which 13 statistical features were extracted. Combining 1800 features from feature extraction with 13 from the deep learning model, we performed a feature selection and subsequently trained a shallow neural network to predict future AF and evaluated its performance on the test cohort. ResultsOn the test set, our model exhibited sensitivity, specificity, and precision of 0.67, 0.81, and 0.3, respectively, for AF prediction. Survival analysis revealed a hazard ratio of 8.36 (p-value: 1.93 × 10−52) for AF outcomes using the log-rank test. ConclusionsOur proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy.

The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements

Photoplethysmography (PPG) is widely used to assess cardiovascular health. Yet, its effectiveness is often hindered by external factors like contact force (CF), which significantly affects the accuracy and reliability of measurements. This study investigates how variations in the CF at the index fingertips influence six signal quality indices (SQIs)—including the perfusion index, skewness, kurtosis, entropy, zero-crossing rate, and relative power—using data from 11 healthy participants. Our analysis of normalized CF values reveals that lower CF ranges (0.2 to 0.4) may be optimal for extracting information about perfusion and blood flow. However, they may not be the best range to capture all the physiological details within the PPG pulse. In contrast, higher CF ranges (0.4 to 0.6) enable capturing more complex signals that could be physiologically representative. The findings underscore the necessity of considering viscoelastic tissue properties and individual biomechanical differences, advocating for both the normalization of CF for improved cross-subject comparison and personalized CF calibration to adapt PPG devices to diverse populations. These strategies ensure measurement reliability and consistency, thereby advancing the accuracy of cardiac and vascular assessments. Our study offers guidelines for adjusting the CF levels to balance signal detail and perfusion quality, customized to meet specific analytical requirements, with direct implications for both clinical and research environments.

Optimal signal quality index for remote photoplethysmogram sensing

Remote photoplethysmography (rPPG) enables non-invasive monitoring of circulatory signals using mobile devices, a crucial advancement in biosensing. Despite its potential, ensuring signal quality amidst noise and artifacts remains a significant challenge, particularly in healthcare applications. Addressing this, our study focuses on a singular signal quality index (SQI) for rPPG, aimed at simplifying high-quality video capture for heart rate detection and cardiac assessment. We introduce a practical threshold for this SQI, specifically the signal-to-noise ratio index (NSQI), optimized for straightforward implementation on portable devices for real-time video analysis. Employing (NSQI < 0.293) as our threshold, our methodology successfully identifies high-quality cardiac information in video frames, effectively mitigating the influence of noise and artifacts. Validated on publicly available datasets with advanced machine learning algorithms and leave-one-out cross-validation, our approach significantly reduces computational complexity. This innovation not only enhances efficiency in health monitoring applications but also offers a pragmatic solution for remote biosensing. Our findings constitute a notable advancement in rPPG signal quality assessment, marking a critical step forward in the development of remote cardiac monitoring technologies with extensive healthcare implications.

Quality-Aware Signal Processing Mechanism of PPG Signal for Long-Term Heart Rate Monitoring.

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.

From Sleep Patterns to Heart Rhythms: Predicting Atrial Fibrillation from Overnight Polysomnograms.

Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60-90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs. We analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI < 0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features. On the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10 -52 ). Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy.

Estimation of phase distortions of the photoplethysmographic signal in digital IIR filtering

Pre-processing of the photoplethysmography (PPG) signal plays an important role in the analysis of the pulse wave signal. The task of pre-processing is to remove noise from the PPG signal, as well as to transmit the signal without any distortions for further analysis. The integrity of the pulse waveform is essential since many cardiovascular parameters are calculated from it using morphological analysis. Digital filters with infinite impulse response (IIR) are widely used in the processing of PPG signals. However, such filters tend to change the pulse waveform. The aim of this work is to quantify the PPG signal distortions that occur during IIR filtering in order to select a most suitable filter and its parameters. To do this, we collected raw finger PPG signals from 20 healthy volunteers and processed them by 5 main digital IIR filters (Butterworth, Bessel, Elliptic, Chebyshev type I and type II) with varying parameters. The upper cutoff frequency varied from 2 to 10 Hz and the filter order—from 2nd to 6th. To assess distortions of the pulse waveform, we used the following indices: skewness signal quality index (SSQI), reflection index (RI) and ejection time compensated (ETc). It was found that a decrease in the upper cutoff frequency leads to damping of the dicrotic notch and a phase shift of the pulse wave signal. The minimal distortions of a PPG signal are observed when using Butterworth, Bessel and Elliptic filters of the 2nd order. Therefore, we can recommend these filters for use in applications aimed at morphological analysis of finger PPG waveforms of healthy subjects.

Bispectral Index versus the University of Michigan Sedation Scale in assessing sedation depth during pediatric drug-induced sleep endoscopy.

Bispectral Index (BIS) and University of Michigan Sedation Scale (UMSS) were two commonly used methods of monitoring the sedation depth, but their correlation was not clear. The purpose of this study is to ascertain if BIS correlates with UMSS in determining the sedation level during pediatric drug-induced sleep endoscopy (DISE). One-hundred children, aged 36-143 months, with ASA I~II grade, were enrolled. They were subject to general anesthesia for an elective adenotonsillectomy. Two drug regimens were used. After UMSS ≥ 3, the sites of airway obstructions were located by checking the supraglottic airway structures with a fibrous laryngoscope. UMSS scores, BIS values, electromyography (EMG), and signal quality indices (SQIs) were recorded at the pre-medication and pre-DISE baseline (T0), 5 min subsequent to medication administration but prior to DISE initiation (T1), 1 min after DISE was initiated (T2), 1 min after DISE was completed (T3), 1 min subsequent to tracheal intubation (T4), 1 min following extubation (T5), and 30 min past extubation (T6). There were strong correlations between BIS monitor readings and UMSS scores for total and two regimens. Kappa values revealed moderate agreement between BIS and UMSS for total and two regimens. The agreement rates were 67.47% for the total, 61.43% for Regimen 1, and 73.42% for Regimen 2, respectively. BIS correlates with UMSS in determining the sedation level during pediatric DISE for two regimens. BIS might serve as an appropriate indicator of sedation intensity when UMSS could not be used.

A Complete Pipeline for Heart Rate Extraction from Infant ECGs

Infant electrocardiograms (ECGs) and heart rates (HRs) are very useful biosignals for psychological research and clinical work, but can be hard to analyse properly, particularly longform (≥5 min) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open source ECG approaches are tested on infant datasets. The best-performing open source method is then modified to maximise its performance on infant data (e.g., including a 15 Hz high-pass filter, adding local peak correction). The HR signal is then subsequently analysed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HRs. A Signal Quality Index (SQI) for HR is also developed, providing insights into where a signal is recoverable and where it is not, allowing for more confidence in the analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for the future analysis of infant ECGs and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world, large datasets.

Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration

Objective. We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal. Methods. We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection. Results. The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation ( γ -score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance. Conclusion. The sync-ensembled EDR provides robust respiratory information from electrocardiogram. Significance. Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

A Novel ECG Signal Quality Index Method Based on Skewness-MODWT Analysis

A Novel ECG Signal Quality Index Method Based on Skewness-MODWT Analysis

Comparison of post-auricular and frontal bispectral index values obtained during renal surgeries

BackgroundThe bispectral index (BIS) monitor is one of the EEG-derived monitoring techniques and well-established devices used to measure the depth of anesthesia. This study aimed to assess the agreement of BIS values based on the positions of either post-auricular or frontal sensors in individual patients undergoing renal surgeries while lateral positions at various stages of anesthesia.Patients and methods12 patients older than 18 years, ASA I-III patients scheduled for elective renal operations, two BIS were placed on each patient, one on each side of the post-auricular region and one across the forehead, and each sensor was connected to a different BIS monitor. We gathered three pieces of data at each of the six-time points: BIS score, signal quality index (SQI) score calculating the signal’s strength and electromyography (EMG) score: before the onset of anesthesia (awake) when the eyelash reflex is lost (LOC), after intubation (intubation), following the initial surgical incision, each 30 min throughout the procedure (maintenance), and at the moment the patient’s eyes open naturally after waking up from anesthesia (emergence).ResultsThe overall BIS value at the frontal position was significantly higher than the post-auricular position (52.5 ± 22.2 and 52.1 ± 22.1, respectively, P = 0.010). On the other hand, the BIS value was comparable between the frontal and post-auricular positions at LOC, intubation, 60, 120, and 80 min and at emergence. A strong link between the two sensor positions, as indicated by the correlation coefficient (r = 0.607, P < 0.001), and the Bland-Altman analysis revealed a small mean difference (-1.8) and a low (9.0/- 12.5) limit of agreement, with just 4.3% of the readings falling outside of it during the anesthetic maintenance period.ConclusionAcceptable variation in BIS data was observed when obtained from the two different sensor positions for clinical usage. The post-auricular BIS sensor system may be a suitable substitute for an impractical frontal setup.Protocol RegistrationThe study was registered in clinicaltrials.gov on 11/07/2022 (trial registration number: NCT05451823).

A Signal Quality Index for Improving the Estimation of Breath-by-Breath Respiratory Rate During Sport and Exercise

A Signal Quality Index for Improving the Estimation of Breath-by-Breath Respiratory Rate During Sport and Exercise

A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal.

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

Unraveling the relationship between heart rate and agitation in people with dementia

AbstractBackgroundAgitation is a critical behavioral and psychological symptom of dementia, leading alone to increased caregiver burden, more frequent hospitalization, and earlier institutionalization. The results presented here provide preliminary insights into one facet of multimodal agitation detection, specifically analyzing the relationship between heart rate and agitation.MethodTo date, 26 people with dementia have been included in our study to detect agitation using a multimodal approach1. In this analysis, heart rate is derived from the wristwatch wearable measuring photoplethysmography (PPG). Data points were included in subsequent analysis if the signal quality index of the original PPG signal was &gt; = 0.8 (on a scale from 0‐1). For each complete survey, the mean heart rate (HR) was extracted from 1‐minute intervals in a 6‐minute window centered on the survey timestamp (± 3 minutes). For each mean HR per minute, the presence or absence of agitation was derived from the Pittsburgh Agitation Scale integrated into the survey. The data was centered within‐patient. Due to the nested structure of the data, hierarchical mixed models were used to predict presence of agitation as a binary response variable.ResultThe resulting dataset of high‐quality HR data consists of 1286 data points, from 21 patients (Age mean: 81 (8.3), nfemale: 8) The 3‐parameter model built included mean heart rate as an explanatory fixed effect, and two random intercepts: day and patient. In the model, increased HR compared to a participant’s average HR over a week, was significantly associated with an increase in odds of agitation (p&lt; 0.001). Every 1 beat increase in mean HR over a minute, from the participants’ mean heart rate, increased the odds of that 1‐minute interval indicating agitation by 7.68% (CI: 5.43‐10.1).ConclusionThese results provide insight into the relationship between heart rate and agitation, possibly indicating activation of the autonomic nervous system, due to the association of increased HR with increased odds of agitation. Analyses like these provide the basis for future agitation detection algorithms that account for a multi‐faceted patient response.1. Davidoff, et al, 2022, Innovation in Aging, Volume 6, Issue 7, 2022, igac064, https://doi.org/10.1093/geroni/igac064

Analysis of Breathing Rate Estimation Using Adaptive Techniques

Breathing rate (BR) is an important physiological factor that is commonly measured in many treatment settings. Though it is still routinely measured by hand. In this study, a novel approach for determining the BR from an ECG, photoplethysmogram, or blood pressure signal is proposed. To extract respiratory signals from time and frequency domain data, the framework employs Discrete Wavelet Transform and Empirical Mode Decomposition techniques. Because we used a Robust Kalman Filter with a Signal Quality Index, our technique worked effectively even when the signals were severely damaged. The output signals have been integrated by state vector fusion, and the BR has been established. Two openly available clinical databases, the MIT-BIH Polysomnographic and the BIDMC datasets are used. The mean absolute percentage error was used to assess performance. The results were very accurate; PPG signals had MAPEs of 7% and BP signals of 5.4%, whereas ECG signals on the two databases had MAPEs of 4% and 4%, respectively.Additionally,the results revealed an astounding robustness to noise at 0 db. In light,this technique may be beneficial for BR monitoring in noisy areas.