Myocardial Ischemia Events Detection based on Support Vector Machines using QRS and ST Features

Magrans, Rudys; Gomis, Pedro; Caminal, P.

doi:10.22489/cinc.2016.117-295

Cited by 2 publications

(3 citation statements)

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“…Specifically, we use support vector machine (SVM) implemented with Scikit Python toolbox [77] and convolutional neural network (Con-vNet) implemented in tensorflow [78], both of which are supervised learning. These toolboxes are used to implement reduced versions of the approaches presented in [22] and [23], respectively. In contrast to these approaches, ours is an unsupervised approach.…”

Section: Comparison With Other Learning-based Heart-rate Estimation T...mentioning

confidence: 99%

“…Similar to the previous approach, this technique uses filters as a pre-processing step with a centering approach for adjusting the R-peak positions. Support vector machine is used in [22] to classify QRS segments.…”

Section: Introductionmentioning

confidence: 99%

“…To validate our approach we used CARLsim [51] spiking neural network simulator with ECG data from in-house clinical trials and also open-source databases. We compared our approach with (1) deterministic QRS detection technique of [6]; (2) the neural network based technique of [23]; and the support vector machine based technique of [22] 2 . Results demonstrate high accuracy of our approach, with applicability to subjects with and without cardiac irregularities.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout

et al. 2018

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Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization.Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases.Results show high accuracy and low energy footprint in heart-rate estimation across * Corresponding author

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Section: Comparison With Other Learning-based Heart-rate Estimation T...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout

et al. 2018

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Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram

Terzi,

Arikan

2023

Biomedical Engineering / Biomedizinische Technik

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Objectives Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs. Methods In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs. Results Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs. Conclusions The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians’ success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).

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Myocardial Ischemia Events Detection based on Support Vector Machines using QRS and ST Features

Cited by 2 publications

References 8 publications

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout

Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram

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