ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure

Abstract

Usually, heart failure occurs when heart-related diseases are developed and continue to deteriorate veins and arteries. Heart failure is the final stage of heart disease, and it has become an important medical problem, particularly among the aging population. In medical diagnosis and treatment, the examination of heart failure contains various indicators such as electrocardiogram. It is one of the relatively common ways to collect heart failure or attack related information and is also used as a reference indicator for doctors. Electrocardiogram indicates the potential activity of patient’s heart and directly reflects the changes in it. In this paper, a deep learning-based diagnosis system is presented for the early detection of heart failure particularly in elderly patients. For this purpose, we have used two datasets, Physio-Bank and MIMIC-III, which are publicly available, to extract ECG signals and thoroughly examine heart failure. Initially, a heart failure diagnosis model which is based on attention convolutional neural network (CBAM-CNN) is proposed to automatically extract features. Additionally, attention module adaptively learns the characteristics of local features and efficiently extracts the complex features of the ECG signal to perform classification diagnosis. To verify the exceptional performance of the proposed network model, various experiments were carried out in the realistic environment of hospitals. Influence of signal preprocessing on the performance of model is also discussed. These results show that the proposed CBAM-CNN model performance is better for both classifications of ECG signals. Likewise, the CBAM-CNN model is sensitive to noise, and its accuracy is effectively improved as soon as signal is refined.

Automatic Identification of Involuntary Muscle Activity in Subacute Patients with Upper Motor Neuron Lesion at Rest—A Validation Study

“…In the literature, algorithm accuracy for automatic classification of bioelectric signals is higher than the one in our study when this detection is related to critical conditions in the patient’s life. The detection of heart failure, pacing artifacts, or noise with electrocardiography (ECG) is performed with algorithms whose accuracy ranges from 97% to 100% [ 26 , 27 , 28 ]. Conversely, algorithms used in non-critical applications have an accuracy similar to that of the algorithm analyzed in this study.…”

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

“… 12-Lead ECG CNN, bi-LSTM Patient-specific Myocardial Infarction Che et al [ 57 ] 2021 Dalian (China) Journal article 3699 records from males and 3178 from females An end-to-end deep learning framework based on convolutional neural networks is proposed for ECG signal processing and arrhythmia classification. 12-Lead ECG CNN Patient-specific Arrhythmia Chen et al [ 22 ] 2021 Wuhan (China) Journal article Intensive Care Medicine Database (61,532 patients) A deep learning-based diagnosis system is proposed for the early detection of heart failure particularly in elderly patients. 2-Lead ECG CNN Patient-specific Heart Failure Dai et al [ 58 ] 2021 Hsinchu (Taiwan) Journal article PTB Diagnostic ECG [ 29 ] database (233 subjects) A deep convolutional neural network to classify five CVDs using standard 12-Lead ECG signals is proposed.…”

“…Also, Artificial Intelligence is a computer science field that allows the creation of solutions for the automatic diagnosis of different diseases based on the data acquired from the sensors [ [19] , [20] , [21] ]. It included identifying different abnormal patterns of the data obtained from the sensors, allowing the identification of various diseases [ [22] , [23] , [24] , [25] ]. Currently, this type of solution is scarce and under development, but it is essential to give autonomy to the patients [ 13 , 26 , 27 ].…”

Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure

“…The data for the predictive variables selected in this paper were obtained from two tables: admission table and chartevents table in the MIIMIC-III database. As a result of the study, we referred to the predictive variables that have been used by other researchers to predict HF mortality in patients [ 2 , 3 , 47 , 48 , 50 ] by measuring clinical symptoms 24 h before admission to the ICU, including Heart Rate, Respiratory Rate, Diastolic Blood Pressure, Systolic Blood Pressure, Temperature, Oxygen Saturation, Blood Urea Nitrogen, Creatinine, Mean Blood Pressure, Glucose, White Blood Cell, Red Blood Cell, Prothrombin Time, International Normalized Ratio, Platelets, GCS Eye, GCS Motor, GCS Verbal, and Patient’s Age and Gender.…”

“…The majority of CVD morbidity and mortality are derived from Heart Failure (HF), a common cardiovascular disease in which the heart fails to maintain the body’s metabolism. Patients with HF experience a variety of overt symptoms such as shortness of breath, swollen ankles, and physical fatigue, and may also show signs of elevated jugular venous pressure, pulmonary fissures, and peripheral edema caused by cardiac or noncardiac structural abnormalities [ 2 ]. As a major cause of cardiovascular morbidity and mortality, HF poses a significant threat to human health and social development [ 3 ].…”