Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals

Sudarshan, Vidya K.; Acharya, U. Rajendra; Oh, Shu Lih; Adam, Muhammad; Tan, Jen Hong; Chua, Chua Kuang; Chua, Kok Poo; Tan, Ru San

doi:10.1016/j.compbiomed.2017.01.019

Cited by 74 publications

(40 citation statements)

References 49 publications

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“…CCORDING to the World Health Organization (WHO), as of 2016, cardiovascular diseases (CVDs) are the main cause of deaths globally [1]. An estimated 17.9 million succumbed to CVD-related ailments, representing 31% of all deaths worldwide in the same year.…”

Section: Introductionmentioning

confidence: 99%

“…An estimated 17.9 million succumbed to CVD-related ailments, representing 31% of all deaths worldwide in the same year. Two-thirds of these deaths occurred in low-income countries [1] where medical facilities for early detection and supports for patients of CVD is deemed generally poor. Whereas the most CVD cases can be prevented by behavioral and lifestyle changes, early detection of such cases presents the next best approach to accurate diagnosis, effective treatment, and management of CVDs.…”

Section: Introductionmentioning

confidence: 99%

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A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

165

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Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECGbased CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

165

View full text Add to dashboard Cite

show abstract

“…Previous ECG-based methods for automated computer aided detection (CAD) of HF using various methodologies have achieved encouraging results. The duration of ECG signal recording was variable and could be as short as 2 s [10,[54][55][56][57][58]. Simple clinical prediction rules (CPR) for HF detection performed less well compared to ECG based methods [59].…”

Section: Discussionmentioning

confidence: 99%

A Prototype Photoplethysmography Electronic Device that Distinguishes Congestive Heart Failure from Healthy Individuals by Applying Natural Time Analysis

et al. 2019

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In this paper, a prototype photoplethysmography (PPG) electronic device is presented for the distinction of individuals with congestive heart failure (CHF) from the healthy (H) by applying the concept of Natural Time Analysis (NTA). Data were collected simultaneously with a conventional three-electrode electrocardiography (ECG) system and our prototype PPG electronic device from H and CHF volunteers at the 2nd Department of Cardiology, Medical School of Ioannina, Greece. Statistical analysis of the results show a clear separation of CHF from H subjects by means of NTA for both the conventional ECG system and our PPG prototype system, with a clearly better distinction for the second one which additionally inherits the advantages of a low-cost portable device.

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“…First, most of the existing methods [ 17 , 18 , 19 ] depend on data preprocessing, such as noise removal and R-peak location. We work on obtaining effective input data by using a light preprocessing method and still have the same level or better performance as the latest method.…”

Section: Related Workmentioning

confidence: 99%

A Robust Multilevel DWT Densely Network for Cardiovascular Disease Classification

Zhang

Yang

et al. 2020

Sensors

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Cardiovascular disease is the leading cause of death worldwide. Immediate and accurate diagnoses of cardiovascular disease are essential for saving lives. Although most of the previously reported works have tried to classify heartbeats accurately based on the intra-patient paradigm, they suffer from category imbalance issues since abnormal heartbeats appear much less regularly than normal heartbeats. Furthermore, most existing methods rely on data preprocessing steps, such as noise removal and R-peak location. In this study, we present a robust classification system using a multilevel discrete wavelet transform densely network (MDD-Net) for the accurate detection of normal, coronary artery disease (CAD), myocardial infarction (MI) and congestive heart failure (CHF). First, the raw ECG signals from different databases are divided into same-size segments using an original adaptive sample frequency segmentation algorithm (ASFS). Then, the fusion features are extracted from the MDD-Net to achieve great classification performance. We evaluated the proposed method considering the intra-patient and inter-patient paradigms. The average accuracy, positive predictive value, sensitivity and specificity were 99.74%, 99.09%, 98.67% and 99.83%, respectively, under the intra-patient paradigm, and 96.92%, 92.17%, 89.18% and 97.77%, respectively, under the inter-patient paradigm. Moreover, the experimental results demonstrate that our model is robust to noise and class imbalance issues.

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Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals

Cited by 74 publications

References 49 publications

A Multitier Deep Learning Model for Arrhythmia Detection

A Multitier Deep Learning Model for Arrhythmia Detection

A Prototype Photoplethysmography Electronic Device that Distinguishes Congestive Heart Failure from Healthy Individuals by Applying Natural Time Analysis

A Robust Multilevel DWT Densely Network for Cardiovascular Disease Classification

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