Apparatuses and systems for artifact reduction in electrodermal activity

A wearable physiological monitoring system configured for bounded real-time prediction of central nervous system oxygen toxicity (CNS-OT) of a diver during hyperbaric oxygen exposure, the system comprising:

• a skin-contact electrodermal activity (EDA) sensor configured to acquire an EDA signal from the diver during underwater operation;
• a wearable computing device comprising:
• one or more processors;
• a non-transitory memory;
• and
• processor-executable instructions that, when executed by the one or more processors, cause the wearable computing device to:
• receive the EDA signal comprising motion-induced artifacts generated during underwater movement of the diver;
• segment the EDA signal into bounded-duration temporal windows;
• apply, within a bounded processing interval associated with each temporal window, a trained deep convolutional autoencoder network to the segmented EDA signal to generate an artifact-reduced EDA signal;
• determine, from the artifact-reduced EDA signal, a time-variant spectral sympathetic index comprising a phasic sympathetic activity component associated with autonomic nervous system activation;
• perform signal-quality gating by rejecting temporal windows failing a signal integrity condition associated with motion contamination;
• determine, based on a temporally increasing sympathetic spectral component satisfying a toxicity progression threshold, a predicted CNS-OT condition associated with prolonged hyperbaric oxygen exposure;
• and
• cause generation of a diver warning output at the wearable computing device before onset of a seizure condition associated with CNS-OT;
• wherein:
• the trained deep convolutional autoencoder network is trained using training data comprising:
• (i) EDA signals containing motion artifacts,
• (ii) artifact-reduced reference EDA signals, and
• (iii) EDA signals obtained during hyperbaric oxygen exposure conditions,
• and wherein the wearable computing device performs said processing operations at the wearable computing device in real time during underwater operation without requiring remote processing.

A non-transitory computer-readable media comprising instructions that when executed by a computer implement a method for bounded real-time prediction of central nervous system oxygen toxicity (CNS-OT) in a diver during hyperbaric oxygen exposure, the method comprising:

• acquiring, by a wearable electrodermal activity sensor, an electrodermal activity (EDA) signal from the diver during underwater operation;
• segmenting the EDA signal into bounded-duration temporal windows;
• performing motion-aware artifact reduction on each temporal window using a trained deep convolutional autoencoder network executed locally at a wearable computing device;
• rejecting temporal windows that fail a signal-quality condition indicative of excessive motion corruption;
• generating, from remaining temporal windows, a time-variant sympathetic spectral measurement comprising a phasic sympathetic activation component;
• detecting a temporally progressive increase in the sympathetic spectral measurement relative to a baseline physiological state associated with absence of CNS-OT exposure;
• determining, based on the detected increase satisfying a toxicity threshold, a predicted CNS-OT condition;
• and
• outputting, by the wearable computing device, a diver alert before occurrence of a seizure event.

A method for physiological monitoring for bounded real-time prediction of central nervous system oxygen toxicity (CNS-OT) of a diver during hyperbaric oxygen exposure, the method comprising:

• obtaining an electrodermal activity (EDA) signal comprising motion-induced artifacts generated during underwater movement of the diver using a skin-contact EDA sensor configured to acquire an EDA signal from the diver during underwater operation;
• receiving the EDA signal using a wearable computing device worn by the diver;
• segmenting the EDA signal into bounded-duration temporal windows using the wearable computing device;
• applying, within a bounded processing interval associated with each temporal window, a trained deep convolutional autoencoder network to the segmented EDA signal to generate an artifact-reduced EDA signal using the wearable computing device;
• determining, from the artifact-reduced EDA signal, a time-variant spectral sympathetic index comprising a phasic sympathetic activity component associated with autonomic nervous system activation using the wearable computing device;
• performing signal-quality gating by rejecting temporal windows failing a signal integrity condition associated with motion contamination using the wearable computing device;
• determining, based on a temporally increasing sympathetic spectral component satisfying a toxicity progression threshold, a predicted CNS-OT condition associated with prolonged hyperbaric oxygen exposure using the wearable computing device;
• and
• causing generation of a diver warning output at the wearable computing device before onset of a seizure condition associated with CNS-OT using the wearable computing device;
• wherein:
• the trained deep convolutional autoencoder network is trained using training data comprising:
• (i) EDA signals containing motion artifacts,
• (ii) artifact-reduced reference EDA signals, and
• (iii) EDA signals obtained during hyperbaric oxygen exposure conditions,
• and wherein the wearable computing device performs said processing operations in real time during underwater operation without requiring remote processing.