Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders

Singh, Jaiteg; Hamid, Aamir

doi:10.1007/s12559-022-10042-2

Cited by 10 publications

(2 citation statements)

References 107 publications

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“…With the increasing pressures of modern society and shifts in lifestyle [1], cardiovascular diseases have gradually become a global public health challenge [2][3][4][5]. According to the latest statistics from the World Health Organization, the proportion of cardiovascular diseases in the global disease burden continues to rise [6].…”

Section: Introductionmentioning

confidence: 99%

“…This can more swiftly identify potential challenges patients might face during rehabilitation, offering timely intervention measures. Thanks to advancements in cognitive computing, it is now feasible to construct cognitive models to assist therapists in decision-making and work ows [3,4,25,26]. Speci cally, two core aspects of HRVB therapy-RF selection and real-time training monitoringcan be enhanced through the integration of cognitive models.…”

Section: Introductionmentioning

confidence: 99%

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Boosting Heart Rate Variability Biofeedback Adaptability in Cardiac Rehabilitation with Cognitive Model

Hu,

Medeiros,

Yuan

et al. 2023

Preprint

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Cardiovascular diseases have emerged as a serious threat to global human health. Cardiac Rehabilitation (CR) is increasingly known as a crucial component in the continuum of care for patients with cardiovascular. Heart Rate Variability Biofeedback (HRVB) facilitates the modulation of the patient's Autonomic Nervous System (ANS) non-invasively by enabling Resonance Frequency (RF) breathing of patients, and realizes the treatment of cardiovascular diseases, which provides support to the development of CR. However, it heavily depends on the manual selection of RF and face-to-face guidance of doctors through the traditional therapies of HRVB, which results in the great constraint of HRVB in widespread application and development in home-based CR. Herein, we proposed a remote human-computer collaborative HRVB system that enabled the independent adjustment of RF, which is named "FreeResp". It gets rid of the requirement of manual adjustment of RF by utilizing a simplistic cognitive computational model. Moreover, wearable technology and the Internet of Things (IoT) were integrated to make remote treatments for patients at home possible. FreeResp exhibited commendable consistency with conventional HRVB methods in determining RF values (22/24) among 24 valid training samples. In addition, the results of the one-month home-based RF breathing training test that used FreeResp demonstrated a significant enhancement in the participants' Heart Rate Variability (HRV) (p < 0.05). Therefore, the FreeResp, as a novel healthcare system, offers timely and precise interventions for home-based patients, providing a fresh perspective for the advancement of home-based CR, and pioneering new therapeutic approaches for long-term cardiovascular health management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%