Recorded Electrocardiogram Signals Research Articles

On‐Skin Paintable Water‐Resistant Biohydrogel for Wearable Bioelectronics

AbstractTo achieve accurate monitoring of bioelectrical signals, it is essential to use customizable bioelectrodes that can self‐adapt to the skin's surface topography. Ion‐conducting hydrogel has received significant attention in this field due to its softness, adhesion, and skin‐like mechanical properties. However, these bioelectrodes currently suffer from degradation of adhesion, electrical conductivity, and skin‐compliance when exposed to aqueous environments. This significantly limits the application of bioelectrodes. Herein, a customizable biohydrogel that can be applied on skin or fabric by solvent volatilization for liquid ink‐gel film conversion is reported. The biohydrogel's distinct characteristic of transitioning between a liquid and hydrogel phase establishes superb conformal contact and dynamic compliance with the epidermis. This effectively eliminates motion artifacts and results in lower contact impedance and noise in both static and dynamic states when compared to existing bioelectrodes. The biohydrogel is applied to the cotton fabric to create electrocardiogram (ECG) monitoring garments. These garments enable the acquisition of ECG signals with high accuracy in aqueous environment for over 72 h. Besides, the biohydrogel‐based garments outperform the commercial gel electrodes by 83.5% in signal‐to‐noise ratio. Additionally, the cotton/biohydrogel electrode facilitates multi‐channel, high‐fidelity recording of ECG signals, enabling high‐performance capture and classification of ECG waveforms across multiple channels.

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

An electrocardiogram (ECG) is a continuous electrical signal from the heart that is recorded to understand the activity and condition of the heart. A recorded ECG signal always follows a defined pattern for a normal heart condition. Variation in the normal ECG pattern can be seen in cases of numerous cardiac abnormalities. A recorded ECG is also affected by a number of noises and distortions, resulting in a low SNR. A variation in ECG pattern can lead to incorrect study and improper diagnosis of heart condition. Thus, to perform an efficient analysis, it is necessary to preprocess the ECG waveform. ECG preprocessing requires noise removal and analysis of necessary features needed to study cardiac activity. In this paper, ECG preprocessing is evaluated by using two noise removal techniques, i.e., finite and infinite impulse response. After this, the R-peaks are detected using discrete wavelet transform (DWT), maximal optimal DWT, principal component analysis and independent component analysis. A wavelet transform technique is further proposed using Savitzky-Golay filtering and DWT. The results obtained from the proposed methodology represent the best results compared to those of other methods explicated in this paper.

IoMT-Enabled Stress Monitoring in a Virtual Reality Environment and at Home

Objective: Modern lifestyles are triggering stress at a disproportionate rate for longer periods of time. Chronic or long-lasting stress can pose a risk to our health. Despite advances in physiological recording methods, mental stress remains challenging to quantify and monitor. Methods: We describe an Internet of Medical Things (IoMT) device with electrocardiogram (ECG) recording features. The recorded ECG signal is processed on-the-fly to calculate, in real time, heart rate, heart rate variability, energy expenditure and mental stress. Data are sent to an online platform using a standard Internet of Things (IoT) publish-subscribe messaging transport protocol for continuous monitoring. Results: The system functionality is first validated by performing hardware-in-the-loop measurements connected to a patient simulator. We then monitored induced stress by recording ECG in subjects using liquid metal electrodes performing a plank walking task in a virtual reality (VR) environment with high heights exposure. The results demonstrate our IoMT system’s ability to provide accurate ECG metrics using novel liquid metal electrodes by detecting continuously increased stress values in a VR setting and at-home. Conclusion: The IoMT measurement device presented provides a novel strategy for monitoring stress in real time. Significance: Our work provides the opportunity for future research on psychological stress and emotion regulation within daily life and the physiological mechanisms through which it influences the health of both children and adults.

The Design and Construction of a 12-Channel Electrocardiogram Device Developed on an ADS1293 Chip Platform

An accurate and compact electrocardiogram (ECG) device will greatly assist doctors in diagnosing heart diseases. It will also help to address the increasing number of deaths caused by heart disease. Accordingly, the goal of the project is to design and construct an easy-to-use compact 12-lead electrocardiogram device that communicates with a computer to create a system that can continuously monitor heart rate and which can be connected to allied medical systems. The design is based on an ECG receiver circuit utilizing an IC ADS1293 and an Arduino Nano. The ADS1293 has built-in input Electromagnetic Interference (EMI) filters, quantizers, and digital filters, which help in reducing the size of the device. The software has been created using the C# programming language, with Windows Presentation Foundation (WPF), aiding the collection of the ECG signals from the receiving circuit via the computer port. An ECG Multiparameter Simulator has been used to calibrate the ECG device. Finally, a plan has been developed to connect the arrangement to health systems according to HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) through Representational State Transfer Application Programming Interface (Rest API). The ECG device, completed at the cost of U$169 excluding labor, allows for the signal of 12 leads of ECG signal to be obtained from 10 electrodes mounted on the body. The processed ECG data was written to a JSON file with a maximum recording time of up to three days, managed by a Structured Query Language Server (SQL) Server database. The software retrieves patient data from electrical medical records in accordance with HL7 FHIR standards. A compact and easy-to-use ECG device was successfully designed to record ECG signals. An in-house developed software was also completed to display and store the ECG signals.

Pre-Processing Based ECG Signal Analysis Using Emerging Tools

The objective of the paper is to count the clear beats (R-peaks or QRS complexes) in the Electrocardiogram (ECG) signal after pre-processing step. Among the three selected techniques, Independent Principal Component Analysis (IPCA), Fractional Fourier Transform (FrFT) and Fractional Wavelet Transform (FrWT) have Sensitivity (Se) of 99.92%, 99.95% and 99.98%, Accuracy (Acc) of 99.88%, 99.93% and 99.96%, Detection Error Rate (DER) of 0.110%, 0.066% and 0.036% along with the disturbance of 2.3%, 2% and 1.7% respectively. The lowest DER in FrWT indicates that it outperforms the IPCA and FrFT. The False Negative plus False Positive (FN + FP) by using the three techniques was 32, 25 and 13 while the output Signal-to-Noise Ratio (SNR) was found to be 24.42, 26.19 and 34.37 dB respectively. These results were figured out on Real Time Database (RT DB). The values of output SNR, Se and Acc were recorded to be 34.37 dB, 99.98% and 99.96% while using FrWT based pre-processing on the data from RT DB. The same technique when used on the data from MIT-BIH arrhythmia Database (MB DB) gave the values of 24.81 dB, 99.96% and 99.93% respectively. It has been observed that all the attributes of the components of ECG signals were preserved when filtered with FrFT. On the other hand, the FrWT segregates the motion artefacts and ECG signal in a better way than FrFT. This is due to the fact that in FrWT decomposition takes place on the signal rotated at a selected angle resulting in less computationally complex decompositions at various levels. It reveals that FrWT retains important biology-related information as the originally recorded ECG signal has.

Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis.

We evaluated the validity of a squeeze-and-excitation and multiscaled fusion network (SE-MSCNN) using single-lead electrocardiogram (ECG) signals for obstructive sleep apnea detection and classification. Overnight polysomnographic data from 436 participants at the Sleep Center of the First Affiliated Hospital of Sun Yat-sen University were used to generate a new FAH-ECG dataset comprising 260, 88, and 88 single-lead ECG signal recordings for training, validation, and testing, respectively. The SE-MSCNN was employed for detection of apnea-hypopnea events from the acquired ECG segments. Sensitivity, specificity, accuracy, and F1 scores were assigned to assess algorithm performance. We also used the SE-MSCNN to estimate the apnea-hypopnea index, classify obstructive sleep apnea severity, and compare the agreement between 2 sleep technicians. The SE-MSCNN's accuracy, sensitivity, specificity, and F1 score on the FAH-ECG dataset were 86.6%, 83.3%, 89.1%, and 0.843, respectively. Although slightly inferior to previously reported results using public datasets, it is superior to state-of-the-art open-source models. Furthermore, the SE-MSCNN had good agreement with manual scoring, such that the Spearman's correlations for the apnea-hypopnea index between the SE-MSCNN and 2 technicians were 0.93 and 0.94, respectively. Cohen's kappa scores in classifying the SE-MSCNN and the 2 sleep technicians were 0.72 and 0.78, respectively. In this study, we validated the use of the SE-MSCNN in a clinical environment, and despite some limitations the network appeared to meet the performance standards for generalizability. Therefore, updating algorithms based on single-lead ECG signals can facilitate the development of novel wearable devices for efficient obstructive sleep apnea screening. Yue H, Li P, Li Y, etal. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med. 2023;19(6):1017-1025.

Design and Development of Cloud Based Real Time ECG Anomaly Detection

The functionality of the heart is assessed by analysing the electrocardiogram (ECG) signal. Numerous methods were developed for the accurate detection of heart diseases utilising the recorded ECG signals. However, individuals with serious heart diseases must be constantly observed, especially if there are few indicating bio-signal patterns. Real-time data collection and processing enables early detection of heart diseases and prevent complications. The AD8232 sensor is used in the project along with its interface with the Node MCU for real time acquisition of ECG signal. The intervals of the PQRST wave are thoroughly investigated utilizing a novel windowing approach based on Wavelet Transform. To increase the QoS in the healthcare system, the final findings are displayed on both the system and the cloud interface. In this work, we compared the ECG parameters with those from well-known smartwatches. The outcomes demonstrated a striking match between the ECG parameters acquired using our method and those obtained using smartwatches. This verifies the precision and dependability of our system in monitoring the ECG parameters from ECG signal. Our methodology has the potential to be a dependable and practical solution for heart rate monitoring in real-time and distant healthcare applications because our algorithm and smartwatches consistently report heart rate rates in the same range.

Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence

Objective: Symptom-driven electrocardiogram (ECG) recording plays a significant role in the detection of post-ablation atrial fibrillation recurrence (AFR). However, making timely medical contact whenever symptoms occur may not be practical. Herein, a deep learning (DL)-based handheld device was deployed to facilitate symptom-driven monitoring. Methods: A cohort of patients with paroxysmal atrial fibrillation (AF) was trained to use a DL-based handheld device to record ECG signals whenever symptoms presented after the ablation. Additionally, 24-hour Holter monitoring and 12-lead ECG were scheduled at 3, 6, 9, and 12 months post-ablation. The detection of AFR by the different modalities was explored. Results: A total of 22 of 67 patients experienced AFR. The handheld device and 24-hour Holter monitor detected 19 and 8 AFR events, respectively, five of which were identified by both modalities. A larger portion of ECG tracings was recorded for patients with than without AFR [362(330) vs. 132(133), P=0.01)], and substantial numbers of AFR events were recorded from 18:00 to 24:00. Compared to Holter, more AFR events were detected by the handheld device in earlier stages (HR=1.6, 95% CI 1.2–2.2, P<0.01). Conclusions: The DL-based handheld device-enabled symptom-driven recording, compared with the conventional monitoring strategy, improved AFR detection and enabled more timely identification of symptomatic episodes.

Development of Low-Cost Biosignal Acquisition System for ECG, EMG, and EOG

The use of bio-signal is very crucial, providing enormous information concerning health and well-being of the individual. such signals can be measured and monitored by specialized devices to each bio-signal, for instance, the electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). Due to use of such devices, these signals could be utilized for several objectives. As it is observed in the devices of medical detection and Human to Machine Interactions (HCI). This paper presents a low-cost bio-signal collection device which is having the ability to record ECG, EMG, and EOG signals. Furthermore, STM32F103C8 system is used in Analog to Digital Conversion (ADC), with its particular application. An application has been developed in order to allow admins to observe and save the data signal simultaneously. This application has been developed by using C++ programming language and MATLAB’s code. The data signal is recorded in a format of mat file, which can be studied in details in the proposed system. This system is capitalized on Universal Serial Bus (USB) wired communication link, which is used to transmit the bio-signal through, that guarantees the safety ,avoid noise and interference. The system shows its compatiblity with various operating systems, such as, Windows, Linux, and Mac.

CRT-Net: A generalized and scalable framework for the computer-aided diagnosis of Electrocardiogram signals

Electrocardiogram (ECG) signals play critical roles in the clinical screening and diagnosis of many types of cardiovascular diseases. Despite the fact that deep neural networks have greatly facilitated the computer-aided diagnosis (CAD) in many clinical tasks, the variability and complexity of ECG in practical scenarios still pose significant challenges in diagnostic performance and clinical applications, especially under the current demand of medical cloud computing and remote diagnosis. In this paper, we propose a generalized and scalable framework for the clinical recognition of ECG. Considering the fact that hospitals generally record ECG signals in the form of graphic waves of 2-D images, we first extract the graphic waves of 12-lead images into numerical 1-D ECG signals by a proposed bi-directional connected method. Subsequently, a novel deep neural network, named CRT-Net, is designed to explore waveform features, morphological characteristics, and time-domain features of ECG by embedding convolution neural network (CNN), recurrent neural network (RNN), and transformer module in a scalable deep model, which is especially suitable in clinical scenarios with different lengths of ECG signals captured from different devices. The proposed framework is first evaluated on two widely investigated public repositories, demonstrating superior performance to the state-of-the-art. We also evaluate the effectiveness of the developed framework on clinically collected ECG images from a local hospital. Moreover, based on the proposed algorithms, we have developed a cloud-based ECG system for the computer-aided diagnosis of different cardiovascular diseases.

Classification of Atrial Fibrillation and Congestive Heart Failure Using Convolutional Neural Network with Electrocardiogram

Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

Breastfeeding duration and vagal regulation of infants and mothers

BackgroundVagal regulation within the parasympathetic nervous system supports self-regulation and social engagement. Research has suggested that early feeding practices may influence mother-infant vagal functioning. AimWe aimed to examine the association between breastfeeding duration and mother-infant vagal regulation during an interaction. We hypothesized that breastfeeding duration would be positively associated with adaptive vagal regulation in infants and mothers. Method204 mother-infant dyads (infant mean age 6 months) completed the face-to-face still-face (FFSF) task. During the task, vagal regulation indexed by respiratory sinus arrhythmia (RSA, a component of heart rate variability) was calculated through continuously recorded electrocardiogram signals. Breastfeeding duration and demographic information were reported by mothers. Linear mixed models were adopted to estimate the associations between breastfeeding duration and repeated measures of RSA in infants and mothers throughout the FFSF. ResultsInfants breastfed for a longer duration demonstrated lower RSA throughout the FFSF (B = −0.06, 95 % CI: −0.09 to −0.03), suggesting active physiological mobilization to engage in interaction and regulate distress. Mothers with longer breastfeeding duration displayed higher baseline RSA and higher overall RSA levels (higher vagal control) during the FFSF (B = 0.06, 95 % CI: 0.02 to 0.09), indicating a calmer and regulated state. ConclusionLonger breastfeeding duration was associated with more adaptive mother-infant vagal regulation during the interaction, indicating a positive dose-response association. This finding reveals breastfeeding practice as a factor contributing to infant effective vagal regulation and further supports WHO recommendation on exclusive breastfeeding for the first 6 months and continuing breastfeeding to 24 months and beyond.

Quantification of changes in myocardial T1 * values with exercise cardiac MRI using a free-breathing non-electrocardiograph radial imaging.

To develop and evaluate a free breathing non-electrocardiograph (ECG) myocardial T1 * mapping sequence using radial imaging to quantify the changes in myocardial T1 * between rest and exercise (T1 *reactivity ) in exercise cardiac MRI (Ex-CMR). A free-running T1 * sequence was developed using a saturation pulse followed by three Look-Locker inversion-recovery experiments. Each Look-Locker continuously acquired data as radial trajectory using a low flip-angle spoiled gradient-echo readout. Self-navigation was performed with a temporal resolution of ∼100 ms for retrospectively extracting respiratory motion. The mid-diastole phase for every cardiac cycle was retrospectively detected on the recorded electrocardiogram signal using an empirical model. Multiple measurements were performed to obtain mean value to reduce effects from the free-breathing acquisition. Finally, data acquired at both mid-diastole and end-expiration are picked and reconstructed by a low-rank plus sparsity constraint algorithm. The performance of this sequence was evaluated by simulations, phantoms, and in vivo studies at rest and after physiological exercise. Numerical simulation demonstrated that changes in T1 * are related to the changes in T1 ; however, other factors such as breathing motion could influence T1 * measurements. Phantom T1 * values measured using free-running T1 * mapping sequence had good correlation with spin-echo T1 values and was insensitive to heart rate. In the Ex-CMR study, the measured T1 * reactivity was 10% immediately after exercise and declined over time. The free-running T1 * mapping sequence allows free-breathing non-ECG quantification of changes in myocardial T1 * with physiological exercise. Although, absolute myocardial T1 * value is sensitive to various confounders such as B1 and B0 inhomogeneity, quantification of its change may be useful in revealing myocardial tissue properties with exercise.

Non-Contact Electrocardiograms Acquisition Method Based on Capacitive Coupling

Electrocardiogram (ECG) monitoring is an essential method for medical diagnosis [1], [2]. With the development of technology, better quality physiological signals can be easily recorded [3]. Long-term monitoring of electrocardiographic activity is helpful to monitor potential cardiac dysfunction. The Ag/Agcl electrode with conductive gel can record ECG signals with good signal quality and has been widely used in hospitals. However, the gel needs to be replaced regularly to achieve good signal quality because the quality of the signal deteriorates due to the gel drying over a long time. In addition, wet electrodes may not be suitable for long-term ECG monitoring because they may cause skin redness, swelling, and discomfort, especially for those patients with allergic reactions. Also, direct contact between electrodes and the human body is not applicable in many cases, such as skin injury patients, infants, etc. The goals of disease monitoring technology based on physiological signals are to reduce the amount invasive of equipment, increase the comfort of patients and improve the quality of life while pursuing high-quality signals.

Arrhythmia classification based on improved monarch butterfly optimization algorithm

Heart attack is a major cause of mortality all over the world also heart-related diseases have increased the expenditure of health care. Electrocardiogram (ECG) is one of the simple ways to diagnose heart diseases. Arrhythmia is characterised by abnormalities in the rhythmic pace of heart, which may occur on a random basis in a everyday life of people. It was necessary to use long-term ECG recording devices to capture these rare occurrences. Morphological feature and wavelet coefficients-based features obtained from the recorded ECG signals. The feature vector is optimized by an Improved Monarch Butterfly optimization (IMBO) algorithm to reduce the dimensionality. These optimized features are applied to convolution neural networks to classify signals. The experimental results of the proposed method give a 99.49% accuracy, 99.58% sensitivity, and 98.83% specificity which is comparative to previous methods, and found better classification accuracy which may help the physician for diagnosing an arrhythmia.

Method for Cardiointervalogram Selection from a Photoplethysmogram Signal for Estimating the Total Percentage of Phase Synchronization of Contours of Autonomic Circulation Regulation

In this paper, we compare four methods of extracting the sequence of intervals between heartbeats from the photoplethysmogram signal to estimate the total percentage of phase synchronization of autonomic regulation circuits of blood circulation that are designed to operate in real time. In the analysis of the experimental ensemble of records of healthy subjects, the optimal parameters for the used approaches were selected. We compared cardiointervalograms obtained from photoplethysmogram signals with cardiointervalograms isolated from simultaneously recorded electrocardiogram signals during the analysis of phase synchronization between the phases of the extracted signals in the low-frequency range (0.04–0.15 Hz), which reflects the element activity of autonomic control of blood circulation. The operability of the approaches used in the analysis of the total percentage of phase synchronization of the autonomic regulation circuits of the cardiovascular system based on the photoplethysmogram signal for groups of healthy people and patients during COVID-19 was demonstrated.

A New Attack Method Against ECG-Based Key Generation and Agreement Schemes in Body Area Networks

Body Area Networks (BAN) are wireless networks designed for deployment on or within the human body. These networks are primarily intended for application within themedical domain due to their capabilities for enablingwireless monitoring of physiological signals, and remote administration of medical devices. Due to their intended use case, securing these devices is paramount. In recent years, several key generationand agreement schemes that rely upon physiological signals of the wearer are developed. However, we have found that the application of Electrocardiogram(ECG) signals in this context may not be appropriate due to a potential vulnerability, wherein previously recorded ECG signals could be used against current and future key agreement attempts to compromise their security. This is a violation of temporal variance which is one of a few properties that make ECG signals suitable for use in key agreement schemes. By extracting the QRS complex from prior recordings and distributing them apart from one another we can construct synthetic signals that have a high level of coherence, and thus allow for the key to be intercepted. Based on the conducted experiments we have found that the proposed attack method yields a 0.7 coherence level regardless of how far away the adversary is from the target. Thismakes the success of such an attack extremely likely and is therefore a real threat to the security of these schemes.

AF episodes recognition using optimized time-frequency features and cost-sensitive SVM.

Although atrial fibrillation (AF) Arrhythmia is highly prevalent within a wide range of populations with major associated risks and due to its episodic occurrence, its recognition remains a challenge for doctors. This paper aims to present and experimentally validate a new efficient approach for the detection and classification of this cardiac anomaly using multiple Electrocardiogram (ECG) signals. This work consists of applying Stockwell transform (ST) with compact support kernel (ST-CSK) for ECG time-frequency analysis. The estimation of the atrial activity (AA) is then achieved after analyzing P-waves of the ECG signals for each heartbeat. ECG signals segmentation allows characterizing the AA by making use of its (t,f) flatness, (t,f) flux, energy concentration and heart rate variability. The features matrix is employed as an input of the support vector machines (SVM) working in binary and asymmetrical mode with an embedded reject option. The proposed algorithm is trained and then tested using different ECG sources namely two databases provided by PhysionNet (MIT-BIH Arrhythmia, MIT-BIH Atrial Fibrillation) and recorded ECG signals using MySignals HW development platform with raspberry Pi 3 model B[Formula: see text]. The used method has achieved [Formula: see text] and [Formula: see text] as sensitivity and specificity, respectively. The obtained results confirm that the proposed approach represents a promising tool for Atrial Fibrillation Episodes (AFE) recognition with significant separability between Normal atrial activity and atrial activity with AF even under real and clinical conditions.

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm.

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15-30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

From ECG signals to images: a transformation based approach for deep learning.

Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).