Special Issue “Computer Aided Diagnosis Sensors”

El-Baz, Ayman; Giridharan, Guruprasad A.; Shalaby, Ahmed; Mahmoud, Ali; Ghazal, Mohammed

doi:10.3390/s22208052

Cited by 1 publication

(1 citation statement)

References 34 publications

(34 reference statements)

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“…Computer-aided diagnosis based on heart rate has been widely used for several years, depending on the device's rules or pattern recognition algorithm. However, with the advancement of artificial intelligence, such as machine learning and deep learning algorithms, the ECG data, image, and the features acquired from it can be used as input and classified with different output classes, such as different heart disease and other health conditions [2][3][4][5]. Recently, researchers trained and tested computer models that could interpret ECG patterns using internet data.…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Electrocardiogram Analysis for Disease Diagnosis

Khan Mamun,

Elfouly

2023

ASI

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Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis or monitoring are based on expert knowledge and rule-centered algorithms. In recent years, with the advancement of artificial intelligence, more and more researchers are using deep learning (ML) and deep learning (DL) with ECG data to detect different types of cardiac issues as well as other health problems such as respiration rate, sleep apnea, and blood pressure, etc. This study presents an extensive literature review based on research performed in the last few years where ML and DL have been applied with ECG data for many diagnoses. However, the review found that, in published work, the results showed promise. However, some significant limitations kept that technique from implementation in reality and being used for medical decisions; examples of such limitations are imbalanced and the absence of standardized dataset for evaluation, lack of interpretability of the model, inconsistency of performance while using a new dataset, security, and privacy of health data and lack of collaboration with physicians, etc. AI using ECG data accompanied by modern wearable biosensor technologies has the potential to allow for health monitoring and early diagnosis within reach of larger populations. However, researchers should focus on resolving the limitations.

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Section: Introductionmentioning

confidence: 99%