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Ultra-high-frequency ECG (UHF-ECG) in a range of 500–1,000 Hz has been tested as a new information source for analysis of left-ventricle dyssynchrony and other myocardial abnormalities. The power of UHF signals is extremely low, for which reason an averaging technique is used to improve signal-to-noise ratio. Since ventricle dyssynchrony is different for various QRS complex types, the detected QRS complexes must be clustered into morphology groups prior to averaging. Here, we present a fully-automated method for clustering. The first goal of the method is to separate previously detected QRS complexes into different morphology groups. The second goal is to precisely fit the QRS annotation marks to the exact same position against the QRS shape. The method is based on the Pearson correlation and is optimized for parallel processing. In our application with UHF-ECG data the number of detected groups was 3.24 ± 3.41 (mean and standard deviation over 1,030 records). The method can be used in other areas also where the clustering of repetitive signal formations is needed. For validation purposes, the method was tested on the MIT-BIH Arrhythmia and INCART databases from Physionet with results of purity of 98.24% and 99.50%.

Ultra-high-frequency ECG (UHF-ECG) in a range of 500–1,000 Hz has been tested as a new information source for analysis of left-ventricle dyssynchrony and other myocardial abnormalities. The power of UHF signals is extremely low, for which reason an averaging technique is used to improve signal-to-noise ratio. Since ventricle dyssynchrony is different for various QRS complex types, the detected QRS complexes must be clustered into morphology groups prior to averaging. Here, we present a fully-automated method for clustering. The first goal of the method is to separate previously detected QRS complexes into different morphology groups. The second goal is to precisely fit the QRS annotation marks to the exact same position against the QRS shape. The method is based on the Pearson correlation and is optimized for parallel processing. In our application with UHF-ECG data the number of detected groups was 3.24 ± 3.41 (mean and standard deviation over 1,030 records). The method can be used in other areas also where the clustering of repetitive signal formations is needed. For validation purposes, the method was tested on the MIT-BIH Arrhythmia and INCART databases from Physionet with results of purity of 98.24% and 99.50%.

Ultra-high-frequency ECG (UHF-ECG) in a range of 500–1,000 Hz has been tested as a new information source for analysis of left-ventricle dyssynchrony and other myocardial abnormalities. The power of UHF signals is extremely low, for which reason an averaging technique is used to improve signal-to-noise ratio. Since ventricle dyssynchrony is different for various QRS complex types, the detected QRS complexes must be clustered into morphology groups prior to averaging. Here, we present a fully-automated method for clustering. The first goal of the method is to separate previously detected QRS complexes into different morphology groups. The second goal is to precisely fit the QRS annotation marks to the exact same position against the QRS shape. The method is based on the Pearson correlation and is optimized for parallel processing. In our application with UHF-ECG data the number of detected groups was 3.24 ± 3.41 (mean and standard deviation over 1,030 records). The method can be used in other areas also where the clustering of repetitive signal formations is needed. For validation purposes, the method was tested on the MIT-BIH Arrhythmia and INCART databases from Physionet with results of purity of 98.24% and 99.50%.