Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features.
Sensors (Basel)
; 22(7)2022 Mar 23.
Article
em En
| MEDLINE
| ID: mdl-35408081
ABSTRACT
Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients' heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Eletrocardiografia
/
Cardiopatias Congênitas
Tipo de estudo:
Diagnostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article