Classification techniques for arrhythmia patterns using convolutional neural networks and Internet of Things (IoT) devices

The rise of Telemedicine has revolutionized how patients are being treated, leading to several advantages such as enhanced health analysis tools, accessible remote healthcare, basic diagnostic of health parameters, etc. The advent of the Internet of Things (IoT), Artificial Intelligence (AI) and their incorporation into Telemedicine extends the potential of health benefits of Telemedicine even further. Therefore, the synergy between AI, IoT, and Telemedicine creates diverse innovative scenarios for integrating cyber-physical systems into medical health to provide remote monitoring and interactive assistance to patients. Data from World Health Organization reports that 7.4 million people died because of Atrial Fibrillation (AF), recognizing the most common arrhythmia associated with human heart rate. Causes like unhealthy diet, smoking, poor resources to go to the doctor and based on research studies, about 12 and 17.9 million of people will be suffering the AF in the USA and Europe, in 2050 and 2060, respectively. The AF as a cardiovascular disease is becoming an important public health issue to tackle. By using a systematic approach, this paper reviews recent contributions related to the acquisition of heart beats, arrhythmia detection, IoT, and visualization. In particular, by analysing the most closely related papers on Convolutional Neural Network (CNN) and IoT devices in heart disease diagnostics, we present a summary of the main research gaps with suggested directions for future research

Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG

Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%

Detection of multi-class arrhythmia using heuristic and deep neural network on edge device

Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based on the QRS complex morphology. We proposed an edge device that detects QRS complexes based on variance analysis (QVAT) and the arrhythmia classification based on the QRS complex spectrogram. The classifier uses two-dimensional convolutional neural network (2D CNN) deep learning. We use a single board computer and neural network compute stick to implement the edge device. The outcomes are a prototype device cardiologists use as a supporting tool for analysing ECG signals, and patients can also use it for self-tests to figure out their heart health. To evaluate the performance of our edge device, we tested using the MIT-BIH database because other methods also use the data. The QVAT sensitivity and predictive positive are 99.81% and 99.90%, respectively. Our classifier's accuracy, sensitivity, predictive positive, specificity, and F1-score are 99.82%, 99.55%, 99.55%, 99.89%, and 99.55%, respectively. The experiment result of arrhythmia classification shows that our method outperforms the others. Still, for r-peak detection, the QVAT implemented in an edge device is comparable to the other methods. In future work, we can improve the performance of r-peak detection using the double-check algorithm in QVAT and cross-check the QRS complex detection by adding 1 class to the classifier, namely the non-QRS class

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Detecting and Mitigating Adversarial Attack

Automating arrhythmia detection from ECG requires a robust and trusted system that retains high accuracy under electrical disturbances. Deep neural networks have become a popular technique for tracing ECG signals, outperforming human experts. Many approaches have reached human-level performance in classifying arrhythmia from ECGs. Even convolutional neural networks are susceptible to adversarial examples as well that can also misclassify ECG signals. Moreover, they do not generalize well on the out-of-distribution dataset. Adversarial attacks are small crafted perturbations injected in the original data which manifest the out-of-distribution shifts in signal to misclassify the correct class. However, these architectures are vulnerable to adversarial attacks as well. The GAN architecture has been employed in recent works to synthesize adversarial ECG signals to increase existing training data. However, they use a disjointed CNN-based classification architecture to detect arrhythmia. Till now, no versatile architecture has been proposed that can detect adversarial examples and classify arrhythmia simultaneously. In this work, we propose two novel conditional generative adversarial networks (GAN), ECG-Adv-GAN and ECG-ATK-GAN, to simultaneously generate ECG signals for different categories and detect cardiac abnormalities. The model is conditioned on class-specific ECG signals to synthesize realistic adversarial examples. Moreover, the ECG-ATK-GAN is robust against adversarial attacked ECG signals and retains high accuracy when exposed to various types of adversarial attacks while classifying arrhythmia. We benchmark our architecture on six different white and black-box attacks and compare them with other recently proposed arrhythmia classification models. When considering the defense strategy, the variation of the adversarial attacks, both targeted and non-targeted, can determine the perturbation by calculating the gradient. Novel defenses are being introduced to improve upon existing techniques to fend off each new attack. This back-and-forth game between attack and defense is persistently recurring, and it became significant to understand the pattern and behavior of the attacker to create a robust defense. One widespread tactic is applying a mathematically based model like Game theory. To analyze this circumstance, we propose a computational framework of game theory to analyze the CNN Classifier's vulnerability, strategy, and outcomes by forming a simultaneous two-player game. We represent the interaction in the Stackelberg Game in Kuhn tree to study players' possible behaviors and actions by applying our Classifier's actual predicted values in CAPTCHA dataset. Thus, we interpret potential attacks in deep learning applications while representing viable defense strategies from the Game theoretical perspective

All-in-One, Wireless, Stretchable Hybrid Electronics for Smart, Connected, and Ambulatory Physiological Monitoring

Commercially available health monitors rely on rigid electronic housing coupled with aggressive adhesives and conductive gels, causing discomfort and inducing skin damage. Also, research-level skin-wearable devices, while excelling in some aspects, fall short as concept-only presentations due to the fundamental challenges of active wireless communication and integration as a single device platform. Here, an all-in-one, wireless, stretchable hybrid electronics with key capabilities for real-time physiological monitoring, automatic detection of signal abnormality via deep-learning, and a long-range wireless connectivity (up to 15 m) is introduced. The strategic integration of thin-film electronic layers with hyperelastic elastomers allows the overall device to adhere and deform naturally with the human body while maintaining the functionalities of the on-board electronics. The stretchable electrodes with optimized structures for intimate skin contact are capable of generating clinical-grade electrocardiograms and accurate analysis of heart and respiratory rates while the motion sensor assesses physical activities. Implementation of convolutional neural networks for real-time physiological classifications demonstrates the feasibility of multifaceted analysis with a high clinical relevance. Finally, in vivo demonstrations with animals and human subjects in various scenarios reveal the versatility of the device as both a health monitor and a viable research tool.ope

Neuromorphic computing based on stochastic spiking reservoir for heartbeat classification

Heart disease is the leading cause of mortality worldwide. The precise heartbeat classification usually requires a higher number of extracted features and heartbeats of the same class may also behave differently in patients. This will lead to computation overhead and challenges in hardware implementation due to the large number of nodes utilized in reservoir computing (RC) networks. In this work, a reservoir computing-based stochastic spiking neural network (SSNN) has been proposed for heartbeat rhythm classification, enabling a patient adaptable and more efficient hardware implementation with low computation overhead caused by minimum extracted features. Only a single feature is employed in template matching to achieve patient adaptability with minimal computation overhead. The single feature, QRS complexes, was extracted and fed into the neural reservoir with 20 neurons in a cyclic topology for arrhythmia similarity calculation and classification. 43 recordings of Electrocardiogram (ECG) signals that included both normal and arrhythmic beats from MIT-BIH arrhythmia database obtained from Physio-Net were used in this work. The proposed stochastic spiking reservoir achieves a sensitivity of 99.6% and an accuracy of 96.91%, signifying that the system is accurate and efficient in classifying normal and abnormal arrhythmias

A Context-Responsive LSTM based IoT Enabled E- Healthcare Monitoring System for Arrhythmia Detection

Detecting Arrhythmia, a life-threatening cardiac condition, in real-time is crucial for timely intervention and improved healthcare outcomes. Traditional manual methods for Arrhythmia detection using Electrocardiogram (ECG) signals are error-prone and resource-intensive. To address these limitations, this paper presents an automated system based on the Context Responsive Long Short-Term Memory (CR-LSTM) model for real-time Arrhythmia classification. The system leverages IoT technology to continuously monitor vital signs and effectively combines contextual information with temporal sensor data to accurately discern different types of Arrhythmias. The CR-LSTM model achieves an impressive accuracy of 99.72% in multiclass classification of Arrhythmias, making it a promising solution for dynamic healthcare settings and proactive personalized care