An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification

The aberration in human electrocardiogram (ECG) affects cardiovascular events that may lead to arrhythmias. Many automation systems for ECG classification exist, but the ambiguity to wisely employ the in-built feature extraction or expert based manual feature extraction before classification still needs recognition. The proposed work compares and presents the enactment of using machine learning and deep learning classification on time series sequences. The two classifiers, namely the Support Vector Machine (SVM) and the Bi-directional Long Short-Term Memory (BiLSTM) network, are separately trained by direct ECG samples and extracted feature vectors using multiresolution analysis of Maximal Overlap Discrete Wavelet Transform (MODWT). Single beat segmentation with R-peaks and QRS detection is also involved with 6 morphological and 12 statistical feature extraction. The two benchmark datasets, multi-class, and binary class, are acquired from the PhysioNet database. For the binary dataset, BiLSTM with direct samples and with feature extraction gives 58.1% and 80.7% testing accuracy, respectively, whereas SVM outperforms with 99.88% accuracy. For the multi-class dataset, BiLSTM classification accuracy with the direct sample and the extracted feature is 49.6% and 95.4%, whereas SVM shows 99.44%. The efficient statistical workout depicts that the extracted feature-based selection of data can deliver distinguished outcomes compared with raw ECG data or in-built automatic feature extraction. The machine learning classifiers like SVM with knowledge-based feature extraction can equally or better perform than Bi-LSTM network for certain datasets

Deep learning-based insights on T:R ratio behaviour during prolonged screening for S-ICD eligibility.

Peer reviewedPublisher PD

Deep learning methods for screening patients' S-ICD implantation eligibility

Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently patients' Electrocardiograms (ECGs) are screened over 10 seconds to measure the T:R ratio, determining the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 seconds is not long enough to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behaviour of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation

Deep learning methods for screening patients' S-ICD implantation eligibility.

Acknowledgments The work of Anthony J. Dunn is jointly funded by Decision Analysis Services Ltd. and EPSRC through the Studentship with Reference EP/R513325/1. The work of Alain B. Zemkoho is supported by the EPSRC grant EP/V049038/1 and the Alan Turing Institute under the EPSRC grant EP/N510129/1. The feedback provided by Sion Cave (DAS Ltd) on the initial draft of the paper is gratefully acknowledged.Peer reviewedPublisher PD

A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning

Electrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.</p

ECG waveform dataset for predicting defibrillation outcome in out-of-hospital cardiac arrested patients

The provided database of 260 ECG signals was collected from patients with out-of-hospital cardiac arrest while treated by the emergency medical services. Each ECG signal contains a 9 second waveform showing ventricular fibrillation, followed by 1 min of post-shock waveform. Patients’ ECGs are made available in multiple formats. All ECGs recorded during the prehospital treatment are provided in PFD files, after being anonymized, printed in paper, and scanned. For each ECG, the dataset also includes the whole digitized waveform (9 s pre- and 1 min post-shock each) and numerous features in temporal and frequency domain extracted from the 9 s episode immediately prior to the first defibrillation shock. Based on the shock outcome, each ECG file has been annotated by three expert cardiologists, - using majority decision -, as successful (56 cases), unsuccessful (195 cases), or indeterminable (9 cases). The code for preprocessing, for feature extraction, and for limiting the investigation to different temporal intervals before the shock is also provided. These data could be reused to design algorithms to predict shock outcome based on ventricular fibrillation analysis, with the goal to optimize the defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation and/or drug administration) for enhancing resuscitation. © 202

Modelos de Markov ocultos para la detección temprana de enfermedades cardiovasculares

Introduction: This article, developed between 2022 and 2023 within the framework of Applied Stochastic Processes by the SciBas group at the Universidad Distrital Francisco José de Caldas, focuses on the role of Hidden Markov Models (HMM) in predicting cardiovascular diseases. Problem: The addressed issue is the need to enhance the early detection of heart diseases, emphasizing how HMM can address uncertainty in clinical data and detect complex patterns. Objective: To evaluate the use of Hidden Markov Models (HMM) in the analysis of electrocardiograms (ECG) for the early detection of cardiovascular diseases. Methodology: The methodology comprises a literature review concerning the relationship between HMM and cardiovascular diseases, followed by the application of HMM to prevent heart attacks and address uncertainty in clinical data. Results: The findings indicate that HMM is effective in preventing heart diseases, yet its effectiveness is contingent upon data quality. These results are promising but not universally applicable. Conclusions: In summary, this study underscores the utility of HMM in early infarction detection and its statistical approach in medicine. It is emphasized that HMM is not infallible and should be complemented with other clinical options and assessment methods in real-world situations. Originality: This work stands out for its statistical and probabilistic approach in the application of Hidden Markov Models (HMM) in medical analysis, offering an innovative perspective and enhancing the understanding of their utility in the field of medicine. Limitations: It is recognized that there are limitations, such as dependence on data quality and variable applicability in clinical cases. These limitations should be considered in the context of their implementation in medical practice.Introducción: Este artículo, desarrollado entre 2022 y 2023 en el marco de Procesos Estocásticos Aplicados por el grupo SciBas de la Universidad Distrital Francisco José de Caldas, se enfoca en el papel de las cadenas de Markov ocultas (HMM) en la predicción de enfermedades cardiovasculares. Problema: El problema abordado es la necesidad de mejorar la detección temprana de enfermedades cardíacas, y se destaca cómo las HMM pueden abordar la incertidumbre en los datos clínicos y detectar patrones complejos. Objetivo: Evaluar el uso de modelos de Markov ocultos (HMM) en el análisis de electrocardiogramas (ECG) para la detección temprana de enfermedades cardiovasculares. Metodología: La metodología incluye una revisión de la literatura sobre la relación entre las HMM y las enfermedades cardiovasculares, seguida de la aplicación de HMM para prevenir infartos y abordar la incertidumbre en los datos clínicos. Resultados: Los resultados indican que las HMM son efectivas en la prevención de enfermedades cardíacas, pero su eficacia depende de la calidad de los datos. Estos resultados son prometedores, pero no universales en su aplicabilidad. Conclusiones: En resumen, este estudio destaca la utilidad de las HMM en la detección temprana de infartos y su enfoque estadístico en medicina. Se enfatiza que no son infalibles y deben complementarse con otras opciones clínicas y métodos de evaluación en situaciones reales

Securing internet of medical things with friendly-jamming schemes

The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission