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Presenter: Ping Cheng
Supervisor:

Date: Mon, March 19, 2018
Time: 09:00:00 - 10:00:00
Place: EOW 230

ABSTRACT

ABSTRACT: Abstract—The timely detection of life-threatening ventricular arrhythmias (VAs) is critical for saving a patient's life. General features characterizing ECG waveforms are extracted for VA detection. To take into account the subtle differences in the QRS-complexes among different people, new personalized features are proposed in this paper based on the correlation coefficient between a patient-specific regular QRS-complex template and his/her real-time ECG data. Small sets of the most effiective features are chosen with Support Vector Machines from 11 newly-extracted and 15 previously-existing features, for efficient performance and real-time operation. Our proposed new features aveCC and medianCC are verified to be effective to enhance the performance of existing features under both the record-based and database-based data divisions. Through 50-times random record-based data divisions, all combinations of two features and three features are tested. The top two-feature combination is VFleak and aveCC, which achieves an area under curve value (AUC) of 98.56% ± 0.89%, specificity (SP) of 94.80% ± 2.15% and accuracy (ACC) of 94.66% ± 1.97%; the top three-feature combination is VFleak, MEA and aveCC, which obtains an AUC of 98.98% ± 0.58%, SP of 95.56% ± 1.45%, and ACC of 95.46% ± 1.36%; these results outperform the previous top-two and top-three feature combinations. Similar results are obtained on the database-based data division.