Method and apparatus for beat-space frequency domain prediction of cardiovascular death after acute coronary event

A method for the machine generation of a model for predicting patient outcome following the occurrence of a medical event, the method comprising the steps of:

• obtaining a physiological signal of interest from a plurality of patients having a plurality of outcomes, the physiological signal having a characteristic;
• obtaining a time series of a signal characteristic;
• dividing the time series into a plurality of window segments;
• converting the time series from time-space to an inverse physiological event space;
• computing the power spectrum for a characteristic frequency band for each window segment;
• computing the 90th percentile of the spectral energies across all window segments for each frequency band;
• and
• inputting the data into the L1-regularized logistic regression machine learning program to generate a weighted risk vector.

A method of predicting death within a predetermined period, of a patient following the occurrence of a cardiac event, the method comprising the steps of:

• obtaining an ECG of the patient of interest, the ECG having a plurality of ECG characteristics;
• obtaining a time series of one of the ECG characteristics of the plurality of ECG characteristics;
• dividing the time series into a plurality of window segments;
• converting the time series from time-space to beat-space;
• computing the power in various frequency bands of each window segment;
• computing the 90th percentile of the spectral energies across all window segments for each frequency band;
• and
• computing the risk by multiplying the 90th percentile spectral energies by a weighted risk vector and comparing the result to a predetermined value.