Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

Sopic, Dionisije; Aminifar, Amin; Aminifar, Amir; Atienza, David

doi:10.1109/tbcas.2018.2848477

Cited by 82 publications

(39 citation statements)

References 35 publications

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“…Numerous researchers have proposed different MI detection methods using a different number of leads, specific features, and a variety of classifiers. References [26,31,32] used timedomain features, [33][34][35][36][37] used frequency domain features, and [38][39][40][41] used different entropy measures to detect MI. Dohare et al [26] used duration of P-wave and QRS complex, T wave inversion, and ST-segment variations and applied SVM to detect MI using 14 features.…”

Section: Literature Workmentioning

confidence: 99%

An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

Shahnawaz

Dawood

2021

Mathematical Problems in Engineering

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Myocardial infarction (MI), usually termed as heart attack, is one of the main cardiovascular diseases that occur due to the blockage of coronary arteries. This blockage reduces the blood supply to heart muscles, and a prolonged deficiency of blood supply causes the death of heart muscles leading to a heart attack that may cause death. An electrocardiogram (ECG) is used to diagnose MI as it causes variations like ST-T changes in the recorded ECG. Manual inspection of these variations is a tedious task and also requires expertise as the variations produced by MI are often very short in duration with a low amplitude. Hence, these changes may be misinterpreted, leading to delayed diagnosis and appropriate treatment. Therefore, computer-aided analysis of ECG may help to detect MI automatically. In this study, a robust deep learning model is proposed to detect MI based on heart rate variability (HRV) analysis of ECG signals from a single lead. Ultrashort-term HRV analysis is performed to compute HRV analysis features from time-domain and frequency-domain parameters through power spectral density estimations. Nonlinear HRV parameters are also computed using Poincare Plot, Recurrence Analysis, and Detrended Fluctuation Analysis. A finely tuned customized artificial neural network (ANN) algorithm is applied on 23 HRV features for MI detection and classification. The K-fold validation method is used to avoid any biases in results and reported 99.1% accuracy, 100% sensitivity, 98.1% specificity, and 99.0% F1 for MI detection, whereas 98.85% accuracy, 97.40% sensitivity, 99.05% specificity, and 97.70% F1 score is achieved for classification. Furthermore, the ANN algorithm completed its execution in just 59 seconds that indicates the efficiency of the proposed ANN model. The overall performance in terms of computed evaluation matrices and execution time indicates the robustness and cost-effectiveness of the proposed methodology. Thus, the proposed model can be used for high-performance MI detection, even in wearable devices.

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Section: Literature Workmentioning

confidence: 99%

An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

Shahnawaz

Dawood

2021

Mathematical Problems in Engineering

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“…The over-sampling effect of uniform sampling is critical in the context of resource-constrained medical systems [2], [3] because more data samples means more energy required to process, store, or transmit the acquired data. Such wearable medical devices are extremely limited in terms of processing power, communication bandwidth, memory storage, and battery lifetime.…”

Section: A the Data Overflow Problemmentioning

confidence: 99%

Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems

et al. 2020

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In the context of wearable medical systems, resources are scarce while performance requirements are high. Traditional sampling strategies create large amounts of data, which hinders the device's battery lifetime. However, energy savings are possible when relying on an event-triggered strategy, following the brain example. In this paper, we explore the use of non-Nyquist sampling for cardiovascular monitoring systems, with an in-depth analysis of the performance of a knowledge-based adaptive sampling strategy. By reducing the average sampling rate from 360 Hz down to 13.6 Hz, we can increase the battery lifetime by 4× with a marginal impact on the accuracy of heart-rate analysis.

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“…Existing literature on data mining over distributed platforms incorporate approaches based on cryptographic and secure multiparty computing techniques [16][17][18][19][20]. However, such methods significantly increase communication and computing overhead, making them inefficient and impractical for many real-world scenarios, where we have large-scale data or limited communication and computing features, e.g., in mobile phones or resource-limited wearable devices [21][22][23][24]. Several state-of-the-art solutions, such as [3,25,26], aim to address learning in distributed settings in terms of reducing communication and computational overheads.…”

Section: Introductionmentioning

confidence: 99%

Scalable Privacy-Preserving Distributed Extremely Randomized Trees for Structured Data With Multiple Colluding Parties

Aminifar

Rabbi

Lamo

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Today, in many real-world applications of machine learning algorithms, the data is stored on multiple sources instead of at one central repository. In many such scenarios, due to privacy concerns and legal obligations, e.g., for medical data, and communication/computation overhead, for instance for large scale data, the raw data cannot be transferred to a center for analysis. Therefore, new machine learning approaches are proposed for learning from the distributed data in such settings. In this paper, we extend the distributed Extremely Randomized Trees (ERT) approach w.r.t. privacy and scalability. First, we extend distributed ERT to be resilient w.r.t. the number of colluding parties in a scalable fashion. Then, we extend the distributed ERT to improve its scalability without any major loss in classification performance. We refer to our proposed approach as k-PPD-ERT or Privacy-Preserving Distributed Extremely Randomized Trees with k colluding parties.

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Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

Cited by 82 publications

References 35 publications

An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems

Scalable Privacy-Preserving Distributed Extremely Randomized Trees for Structured Data With Multiple Colluding Parties

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