A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">State of the mind class</td><td class="align_center">Number of samples</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Baseline</td><td class="align_center">274,790</td></tr><tr><td class="align_left">Amusement</td><td class="align_center">117150</td></tr><tr><td class="align_left">Stressed</td><td class="align_center">65450</td></tr><tr><td class="align_left">Meditation</td><td class="align_center">37090</td></tr><tr><td class="align_left">Recovery</td><td class="align_center">79000</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>State of the mind category distribution.</div>

Journal of Healthcare Engineering

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Table 1

Table 1: A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices 

Table 1 | A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices 