Dear Matlab Community,
I am currently working on a classification task with ECG recordings stored in a CSV file with dimensions of (5001, 12). The first row contains headers, and each column represents a lead of the ECG, totaling 12 leads. These recordings were made over a duration of 10 seconds at a sampling frequency of 500 Hz. Therefore, each lead comprises a sequence of 5000 values. The unit of measurement is 0.01 mV, adhering to the Philips standard recording system.
My specific question pertains to the methodology of feature extraction for classification purposes using the Continuous Wavelet Transform (CWT). Should I proceed by creating a scalogram for each lead independently and then concatenating them, or should I generate 12 scalograms from each ECG and consider them as belonging to the same class?
Put differently, is each lead’s scalogram regarded as a class instance, or is it pivotal to concatenate the 12 leads to accurately represent an ECG? Your insights and guidance on this matter would be greatly appreciated.
Sincerely,Dear Matlab Community,
I am currently working on a classification task with ECG recordings stored in a CSV file with dimensions of (5001, 12). The first row contains headers, and each column represents a lead of the ECG, totaling 12 leads. These recordings were made over a duration of 10 seconds at a sampling frequency of 500 Hz. Therefore, each lead comprises a sequence of 5000 values. The unit of measurement is 0.01 mV, adhering to the Philips standard recording system.
My specific question pertains to the methodology of feature extraction for classification purposes using the Continuous Wavelet Transform (CWT). Should I proceed by creating a scalogram for each lead independently and then concatenating them, or should I generate 12 scalograms from each ECG and consider them as belonging to the same class?
Put differently, is each lead’s scalogram regarded as a class instance, or is it pivotal to concatenate the 12 leads to accurately represent an ECG? Your insights and guidance on this matter would be greatly appreciated.
Sincerely, Dear Matlab Community,
I am currently working on a classification task with ECG recordings stored in a CSV file with dimensions of (5001, 12). The first row contains headers, and each column represents a lead of the ECG, totaling 12 leads. These recordings were made over a duration of 10 seconds at a sampling frequency of 500 Hz. Therefore, each lead comprises a sequence of 5000 values. The unit of measurement is 0.01 mV, adhering to the Philips standard recording system.
My specific question pertains to the methodology of feature extraction for classification purposes using the Continuous Wavelet Transform (CWT). Should I proceed by creating a scalogram for each lead independently and then concatenating them, or should I generate 12 scalograms from each ECG and consider them as belonging to the same class?
Put differently, is each lead’s scalogram regarded as a class instance, or is it pivotal to concatenate the 12 leads to accurately represent an ECG? Your insights and guidance on this matter would be greatly appreciated.
Sincerely, ecg, classification, feature extraction, continuous wavelet transform (cwt), scalogram, lead concatenation, signal processing MATLAB Answers — New Questions

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