A Comparative Study of Myocardial Infarction Detection from ECG Data Using Machine Learning

Chakraborty, Aritra; Chatterjee, Santanu; Majumder, Koushik; Shaw, Rabindra Nath; Ghosh, Ankush

doi:10.1007/978-981-16-2164-2_21

Cited by 33 publications

(6 citation statements)

References 33 publications

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“…Moreover, an extension function, “Diagnosis”, is reserved. To realize this function in the future, a well‐trained algorithm, such as a machine learning algorithm, [ 42 ] will be embedded into sensory system to compare the real‐time monitored signal with standard signals marked in health or various illnesses.…”

Section: Resultsmentioning

confidence: 99%

Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring

Zhang

Liu

et al. 2022

Adv Materials Inter

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On‐skin sensors can precisely perceive important electrophysiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG). Despite significant advances in the development of soft materials as electrode sensors, data acquisition (DAQ) unit—another indispensable component of on‐skin electronic sensory systems—typically exhibits bulkiness or unimodal sensing, which is detrimental to the portability of the sensory system or the comprehensiveness of the perceived information. Here, a portable and multimodal DAQ unit to tackle these challenges is designed. By assembling the DAQ unit with low‐impedance (<100 Ω) laser‐induced graphene on‐skin electrode sensors, a wireless communication module, a power supply module, and a 3D printed protective shell, the completed sensory system can realize three‐in‐one monitoring of EEG, ECG, and EMG with a light weight of 22 g and a low cost of $25. Moreover, a mobile App is developed to display the perceived electrophysiological signals in real time. Human–machine interface and embedded machine learning are demonstrated using the designed sensory system, indicating its potential applications in artificial intelligence. The success of this inexpensive three‐in‐one portable electronic sensory system sheds light on design, fabrication, and commercialization of multifunctional wearable electronics with wide applications in fitness tracking, medical diagnostics, and human–machine interface.

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Section: Resultsmentioning

confidence: 99%

Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring

Zhang

Liu

et al. 2022

Adv Materials Inter

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“…The recent research on ECG classification computer-aid systems is based on new techniques of artificial intelligence. Various types of these techniques have been investigated and analyzed for this purpose and other purposes [ 22 , 23 ], such as decision trees [ 24 ], random forest (RF) [ 25 , 26 , 27 ], support vector machine (SVM) [ 28 , 29 ], k-nearest neighbor (KNN) [ 30 ], the hybrid FFPSONeural network classifier [ 31 ], in addition to other methods, such as [ 32 , 33 , 34 ]. In [ 35 ], the authors incorporated two categories, normal and MI, into their investigation.…”

Section: Literature Overviewmentioning

confidence: 99%

A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection

Sraitih

Jabrane²,

Hassani³

2022

JCM

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An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances.

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“…Machine learning techniques have gained prominence in the analysis and interpretation of physiological signals such as PPG and ECG, enabling advancements in detection, classification, and prediction tasks, with potential applications in various domains [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

et al. 2023

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Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model’s capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.

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A Comparative Study of Myocardial Infarction Detection from ECG Data Using Machine Learning

Cited by 33 publications

References 33 publications

Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring

Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring

A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection

A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

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