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

Table 10

Comparative analysis of the model performance based on the optimizer algorithms for subject 1 in the testing set.

Metric	Adam	RMSprop	SGD
Accuracy	97.62	90.45	92.51
Recall “baseline”	0.9861	0.8945	0.9063
Precision “baseline”	0.9703	0.9106	0.9542
F1 score “baseline”	0.9716	0.9033	0.9311
Recall “amusement”	0.9891	0.9322	0.9256
Precision “amusement”	0.9956	0.9158	0.9428
F1 score “amusement”	0.991	0.9288	0.9299
Recall “stress”	0.9832	0.9647	0.9568
Precision “stress”	0.9784	0.94	0.9487
F1 score “stress”	0.9693	0.9561	0.9509
Recall “meditation”	0.9583	0.9428	0.9467
Precision “meditation”	0.9752	0.9022	0.9788
F1 score “meditation”	0.9680	0.9312	0.9635
Recall “recovery”	0.9456	0.9365	0.9387
Precision “recovery”	0.9711	0.9174	0.9579
F1 score “recovery”	0.9620	0.9258	0.9466