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

Table 12

Comparative analysis of the model performance for multichannel CNN for Type-II model.

Metrics	Subject 1	Subject 2	Subject 3	Subject 4	Subject 5	Subject 6	Subject 7	Subject 8	Subject 9	Subject 10	Subject 11	Subject 12	Subject 13	Subject 14	Subject 15
Accuracy	75.34	78.66	74.56	0.7644	75.84	76.54	77.8	79.89	78.26	76.20	76.79	77.72	77.66	78.08	76.14
Recall “baseline”	0.724	0.79	0.741	0.748	0.739	0.873	0.793	0.874	0.814	0.813	0.791	0.831	0.884	0.83	0.719
Precision “baseline”	0.752	0.795	0.771	0.715	0.769	0.718	0.798	0.715	0.79	0.727	0.742	0.809	0.8	0.775	0.713
F1 score “baseline”	0.738	0.792	0.756	0.731	0.754	0.788	0.795	0.787	0.802	0.768	0.766	0.82	0.84	0.802	0.716
Recall “amusement”	0.723	0.75	0.718	0.676	0.669	0.694	0.678	0.705	0.709	0.694	0.676	0.686	0.714	0.682	0.728
Precision “amusement”	0.727	0.813	0.818	0.841	0.81	0.835	0.714	0.769	0.752	0.742	0.777	0.816	0.844	0.79	0.736
F1 score “amusement”	0.725	0.78	0.765	0.75	0.733	0.758	0.696	0.736	0.73	0.717	0.723	0.745	0.774	0.732	0.732
Recall “stress”	0.719	0.734	0.786	0.74	0.804	0.718	0.744	0.744	0.806	0.734	0.767	0.749	0.746	0.747	0.801
Precision “stress”	0.785	0.796	0.769	0.788	0.843	0.781	0.836	0.778	0.813	0.785	0.815	0.844	0.805	0.76	0.838
F1 score “stress”	0.751	0.764	0.777	0.763	0.823	0.748	0.787	0.761	0.809	0.759	0.79	0.794	0.774	0.753	0.819
Recall “meditation”	0.734	0.798	0.775	0.873	0.779	0.756	0.835	0.849	0.759	0.788	0.757	0.819	0.766	0.791	0.773
Precision “meditation”	0.846	0.808	0.822	0.811	0.843	0.822	0.851	0.776	0.778	0.841	0.785	0.834	0.862	0.871	0.887
F1 score “meditation”	0.786	0.803	0.798	0.841	0.81	0.788	0.843	0.811	0.768	0.814	0.771	0.826	0.811	0.829	0.826
Recall “recovery”	0.867	0.861	0.723	0.785	0.801	0.786	0.84	0.819	0.825	0.781	0.849	0.801	0.773	0.854	0.786
Precision “recovery”	0.814	0.841	0.803	0.795	0.837	0.782	0.808	0.836	0.81	0.798	0.795	0.814	0.789	0.785	0.824
F1 score “recovery”	0.84	0.851	0.761	0.79	0.819	0.784	0.824	0.827	0.817	0.789	0.821	0.807	0.781	0.818	0.805