Development of ECG beat segmentation method by combining lowpass filter and irregular R–R interval checkup strategy

We have developed a long-term cardiorespiratory sensor system that includes a wearable sensor probe with adaptive hardware filters and data processing algorithms (Choi and Jiang, 2006 and Choi and Jiang, 2008). However, the data processing algorithm proposed for the R–R interval (RRI) information extraction did not work well in the case of ECG signals with baseline shifts or muscle artifacts. Furthermore, many false ECG beats were extracted due to a weak decision-making scheme. Then, those false beats produced irregular RRI information and erroneous heart rate variability results. Modification of data processing algorithm was strongly needed. Therefore, this work presented an efficient ECG beat segmentation method using an irregular RRI checkup strategy into five sequential RRI patterns. This algorithm was comprised of signal processing stage and ECG beat detector stage. The signal processing included the wavelet denoising, the baseline shift elimination by 20 Hz lowpass filter and the envelope curve extraction by a single degree of freedom analytical model. The ECG beat detector included the candidate ECG beat detection and segmentation by one threshold and by irregular RRI checkup strategy, respectively. In particular, four abnormal RRI patterns were proposed to find out false ECG beats. The MIT-BIH arrhythmia database was selected as the dataset for testing the proposed algorithm. The proposed irregular RRI checkup strategy estimated 5463 beats to the suspected false beats and succeeded in segmenting 96.19% (5255 beats) of them. The performance results showed that our algorithm had very good results such as the detection error of 0.54%, sensitivity of 99.66% and positive predictivity of 99.80%. Furthermore, our algorithm showed very high accuracy as the mean time error between the beat annotations of the database and our obtained beat occurence times was 7.75 ms.