Discrimination of Normal and Abnormal Heart Sounds Using Probability Assessment

Discrimination of Normal and Abnormal Heart Sounds Using Probability Assessment

Stať ve sborníku v databázi WoS či Scopus

Aims: According to the “2016 Physionet/CinC Challenge”, we propose an automated method identifying normal or abnormal phonocardiogram recordings. Method: Invalid data segments are detected (saturation, blank and noise tests) and excluded from further processing. The record is transformed into amplitude envelopes in five frequency bands. Systole duration and RR estimations are computed; 15-90 Hz amplitude envelope and systole/RR estimations are used for detection of the first and second heart sound (S1 and S2). Features from accumulated areas surrounding S1 and S2 as well as features from the whole recordings were extracted and used for training. During the training process, we collected probability and weight values of each feature in multiple ranges. For feature selection and optimization tasks, we developed C# application PROBAfind, able to generate the resultant Matlab code. Results: The method was trained with 3153 Physionet Challenge recordings (length 8-60 seconds; 6 databases). The results of the training set show the sensitivity, specificity and score of 0.93, 0.97 and 0.95, respectively. The method was evaluated on a hidden Challenge dataset with sensitivity and specificity of 0.87 and 0.83, respectively. These results led to an overall score of 0.85.

Aims: According to the “2016 Physionet/CinC Challenge”, we propose an automated method identifying normal or abnormal phonocardiogram recordings. Method: Invalid data segments are detected (saturation, blank and noise tests) and excluded from further processing. The record is transformed into amplitude envelopes in five frequency bands. Systole duration and RR estimations are computed; 15-90 Hz amplitude envelope and systole/RR estimations are used for detection of the first and second heart sound (S1 and S2). Features from accumulated areas surrounding S1 and S2 as well as features from the whole recordings were extracted and used for training. During the training process, we collected probability and weight values of each feature in multiple ranges. For feature selection and optimization tasks, we developed C# application PROBAfind, able to generate the resultant Matlab code. Results: The method was trained with 3153 Physionet Challenge recordings (length 8-60 seconds; 6 databases). The results of the training set show the sensitivity, specificity and score of 0.93, 0.97 and 0.95, respectively. The method was evaluated on a hidden Challenge dataset with sensitivity and specificity of 0.87 and 0.83, respectively. These results led to an overall score of 0.85.

Heart Beat; Normal; Abnormal; CinC challenge

Heart Beat; Normal; Abnormal; CinC challenge

JURČO, J.; PLEŠINGER, F.; JURÁK, P.; HALÁMEK, J.

2017

11.09.2016

IEEE Computer Society

Vancouver

978-1-4799-4346-3

Computing in Cardiology

2016

0276-6574

Computers in Cardiology

Spojené státy americké

801

804

4