Multimodal Cardiorespiratory Monitor

A method comprising:

• collecting a plurality of cardiorespiratory signal measurements from a patient using a plurality of sensors;
• preprocessing the plurality of cardiorespiratory signal measurements to ensure a common length with respect to cardiorespiratory cycles and a common alignment with cardiorespiratory cycles using an electrocardiogram (ECG) signal and a seismocardiogram (SCG) signal as references;
• inputting cardiorepiratory data into a deep learning model trained to classify cardiorespiratory conditions;
• analyzing the cardiorepiratory data using the deep learning model to generate a classification result indicating a likelihood of a diagnosis of a cardiorespiratory condition;
• and
• outputting the classification result, wherein the classification result indicates a cardiorespiratory health status of the patient.

An apparatus comprising:

• a plurality of sensors configured to collect a plurality of cardiorespiratory signal measurements;
• a memory configured to store a deep learning model trained on a dataset comprising a plurality of test subject records, each record including a plurality of cardiorespiratory signal measurements and a corresponding diagnosis of a cardiorespiratory condition;
• and
• a processor operatively coupled to the plurality of sensors and the memory, wherein the processor is configured to:
• preprocess the plurality of cardiorespiratory signal measurements to ensure a common length with respect to cardiorespiratory cycles and a common alignment with cardiorespiratory cycles using an ECG signal and a seismocardiogram (SCG) signal as a reference;
• input cardiorepiratory data into the deep learning model;
• analyze the cardiorepiratory data using the deep learning model to generate a classification result indicating a likelihood of a diagnosis of a cardiorespiratory condition;
• and
• output the classification result, wherein the classification result further indicates a cardiorespiratory health status.