Finding disease similarity by combining ECG with heart auscultation sound

Computers in Cardiology

Volumn 34, Issue , 2007, Pages 261-264

  Wang, F ^a,b   Syeda Mahmood, T ^a   Beymer, D ^a  

^a IBM ALMADEN RESEARCH CENTER   (United States)

^b NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIOLOGY; ELECTROCARDIOGRAPHY; HEART; PATIENT REHABILITATION;

CARDIO-VASCULAR DISEASE; COMMON KNOWLEDGE; DIAGNOSTIC AIDS; DISEASE SIMILARITIES; DYNAMIC TIME WARPING; MORPHOLOGICAL VARIATION; SHAPE SIMILARITY; VISUAL EXAMINATION;

DISEASES;

EID: 62949097240     PISSN: 02766574     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIC.2007.4745471     Document Type: Conference Paper

References (6)

1. Izeboudjen N, Farah A. A new neural network system for arrhythmia's classification. In Neural Computation. 1998; 208-212.
2. de Chazal P, Celler BG, Reilly RB. Using wavelet coefficients for the classification of the electrocardiogram. IEEE Conf on EMBS 2000;1:64-67.
3. Alexakis C, Nyongesa H, Saatchi R, Harris N, Davies C, Emery C, Ireland R, Heller S. Feature extraction and classification of electrocardiogram (ecg) signals related to hypoglycaemia. Computers in Cardiology 2003;30:537-540.
4. Tuzcu V, Nas S. Dynamic time warping as a novel tool in pattern recognition of ecg changes in heart rhythm disturbances. IEEE Conf on SMC 2005;1:182-186.
5. Syeda-Mahmood T, Beymer D, Wang F. Shape-based matching of ecg recordings. IEEE Conf on EMBC 2007;.
6. Alim O, Hamdy N, El-Hanjouri M. Heart diseases diagnosis using heart sounds. Proc National Radio Science Conference 2002;636 - 640.