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Research Article

Early detection of mental disorder signs using photoplethysmogram : A review

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References

  • Ritchie, H. and Roser, M. “Mental Health - Our World in Data,” (2018).
  • Salleh, M. R. “The Burden of Mental Illness: An Emerging Global Disaster,” J. Clin. Heal. Sci., vol. 3, no. 1, p. 5, (2018).
  • Inwood, E. and Ferrari, M. “Mechanisms of Change in the Relationship between Self-Compassion, Emotion Regulation, and Mental Health: A Systematic Review,” Appl. Psychol. Heal. Well-Being, vol. 10, no. 2, pp. 215–235, (2018).
  • Arango, C., Díaz-Caneja, C. M., McGorry, P. D., Rapoport, J., Sommer, I. E., Vorstman, J. A., McDaid, D., Marín, O., Serrano-Drozdowskyj, E., Freedman, R., and Carpenter, W. “Preventive strategies for mental health,” The Lancet Psychiatry, vol. 5, no. 7, pp. 591–604, (2018).
  • Ogunsemi, O. O., Oluwole, F. A., Abasiubong, F., Erinfolami, A. R., Amoran, O. E., Ariba, A. J., Alebiosu, C. O., and Olatawura, M. O. “Detection of mental disorders with the patient health questionnaire in primary care settings in Nigeria,” Ment. Illn., vol. 2, no. 1, pp. 46– 50, (2010).
  • Kroenke, K., Spitzer, R. L., and Williams, J. B. W. “The PHQ-9: Validity of a brief depression severity measure,” J. Gen. Intern. Med., vol. 16, no. 9, pp. 606–613, (2001).
  • Kumari, P., Gupta, P., Piyoosh, A. K., Tyagi B., and Kumar, P., “COVID 19 : Impact on mental health of graduating and post graduating students,” Journal of Statistics and Management Systems, vol. 24, no. 1, pp. 67–79, (2021).
  • Narziev, N., Goh, H., Toshnazarov, K., Lee, S. A., Chung, K. M., and Noh, Y. “STDD: Short-term depression detection with passive sensing,” Sensors (Switzerland), vol. 20, no. 5, pp. 1–18, (2020).
  • Perna, G., Riva, A., Defillo, A., Sangiorgio, E., Nobile, M., and Caldirola, D. “Heart rate variability: Can it serve as a marker of mental health resilience?,” J. Affect. Disord., vol. 263, no. October, pp. 754–761, (2020).
  • Sharma, V. K., and Copeland, J. R. M. “Detecting mental disorders in primary care,” Ment. Health Fam. Med., vol. 6, no. 1, pp. 11–13, (2009).
  • Brueckl, T. M., Spoormaker, Victor I., Saemann, P. G., Brem, A.-K., Hence, L., Czamara, D., and Elbau, I. “The biological classification of mental disorders (BeCOME) study: A protocol for an observational deep-phenotyping study for the identification of biological subtypes,” BMC Psychiatry, vol. 20, no. 1, pp. 1–25, (2020).
  • National Collaborating Centre for Mental Health and Health, Common Mental Health Disorders Identification and Pathways. (2011).
  • Vahey, R., and Becerra, R. “Galvanic skin response in mood disorders: A critical review,” Int. J. Psychol. Psychol. Ther., vol. 15, no. 2, pp. 275– 304, (2015).
  • Anderson, J., Michalak, E., and Lam, R. “Depression in primary care: Tools for screening, diagnosis, and measuring response to treatment | BC Medical Journal,” BC Med. J., vol. 44, no. 8, pp. 415–419, (2002).
  • N. I. of H. (US) and B. S. C. Study, “Information about Mental Illness and the Brain,” (2007).
  • Zamkah, A., Hui, T., Andrews, S., Dey, N., Shi, F., and Sherratt, R. S. “Identification of suitable biomarkers for stress and emotion detection for future personal affective wearable sensors,” BIOSENSORS-BASEL, vol. 10, no. 4, (2020).
  • Sviridova, N., & Sakai, K. “Human photoplethysmogram: New insight into chaotic characteristics,” Chaos, Solitons and Fractals, vol. 77, pp. 53–63, (2015).
  • Kim, T. Y., Ko, H., & Kim, S. H. “Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles,” J. Adv. Transp., vol. (2020).
  • Amira, T., Dan, I., Atta, B., Said, G., Azeddine, B., and Katarzyna, W. W. “Stress level classification using heart rate variability,” Adv. Sci. Technol. Eng. Syst., vol. 4, no. 3, pp. 38–46, (2019).
  • Ismaal, I. H. “Stress dan kesihatan,” Univ. Teknol. Malaysia, (2017).
  • Hsu, Y. C., Li, Y. H., Chang, C. C., and Harfiya, L. N. “Generalized deep neural network model for cuffless blood pressure estimation with photoplethysmogram signal only,” Sensors (Switzerland), vol. 20, no. 19, pp. 1–18, (2020).
  • Elgendi, M. “On the Analysis of Fingertip Photoplethysmogram Signals,” Curr. Cardiol. Rev., vol. 8, no. 1, pp. 14–25, (2012).
  • Tamura, T., Maeda, Y., Sekine, M., and Yoshida, M. “Wearable photoplethysmographic sensors—past and present,” Electron. , vol. 3, no. 2, pp. 282–302, (2014).
  • Tamura, T. “Current progress of photoplethysmography and SPO2 for health monitoring,” Biomed. Eng. Lett., vol. 9, no. 1, pp. 21–36, (2019).
  • Moraes, J. L., Rocha, M. X., Vasconcelos, G. G., Vasconcelos Filho, J. E., de Albuquerque, V. H. C., and Alexandria, A. R. “Advances in photopletysmography signal analysis for biomedical applications,” Sensors (Switzerland), vol. 18, no. 6, pp. 1–26, (2018).
  • Liu, I., Ni, S., and Peng, K. “Enhancing the robustness of smartphone photoplethysmography: A signal quality index approach,” Sensors (Switzerland), vol. 20, no. 7, (2020).
  • Fujita, D., & Suzuki, A. “Evaluation of the Possible Use of PPG Waveform Features Measured at Low Sampling Rate,” IEEE Access, vol. 7, pp. 1–7, (2019).
  • Ab Hamid, H., and Nayan, N. A. “Methods of extracting feature from photoplethysmogram waveform for non-invasive diagnostic applications,” Int. J. online Biomed. Eng., vol. 16, no. 9, pp. 39–62, (2020).
  • Jeyhani, V., Mahdiani, S., Peltokangas, M., and Vehkaoja, A. “ComparisonofHRVparametersderivedfromphotoplethysmography and electrocardiography signals,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2015-November, pp. 5952–5955, (2015).
  • Lamba, P. S., and Virmani, D., “Contactless heart rate estimation from face videos,” Journal of Statistics and Management Systems, vol. 23, no. 7, pp. 1275–1284, (2020).
  • Li, F., Yang, L., Shi, H., and Liu, C. “Differences in photoplethysmography morphological features and feature time series between two opposite emotions: Happiness and sadness,” Artery Res., vol. 18, no. December, pp. 7–13, (2017).
  • Charlton, P.H.”Assessingmentalstressfromthephotoplethysmogram: a numerical study,” Physiol. Meas., pp. 1–16, (2018).
  • Arza, A., Garzón-Rey, J. M., Lázaro, J., Gil, E., Lopez-Anton, R., De La Camara, C., Laguna, P., Bailon, R., and Aguiló, J. “Measuring acute stress response through physiological signals: towards a quantitative assessment of stress,” Med. Biol. Eng. Comput., pp. 1–24, (2019).
  • Preethi, M., Nagaraj, S., and Mohan M. P., “Emotion based Media Playback System using PPG Signal,” in 2021 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2021, (2021).
  • Awasthi, K., Nanda, P., and S. K V, “Performance analysis of Machine Learning techniques for classification of stress levels using PPG signals,” IEEE Explore, (2020).
  • N. E. J. Asha, Ehtesum-Ul-Islam, and R. Khan, “Low-Cost Heart Rate Sensor and Mental Stress Detection Using Machine Learning,” in Proceedings of the 5th International Conference on Trends in Electronics and Informatics, ICOEI 2021, (2021)
  • Ashisha G. R., Mary A. X., and Rose L., “Design challenges for embedded based wireless postoperative bedside monitoring system,” Journal of Interdisciplinary Mathematics, vol. 23, no. 1, pp. 285–292, (2020).
  • Lee, J., Kim, J. and Shin, M. “Correlation Analysis between Electrocardiography (ECG) and Photoplethysmogram (PPG) Data for Driver’s Drowsiness Detection Using Noise Replacement Method,” Procedia Comput. Sci., vol. 116, pp. 421–426, (2017).
  • Georgieva-Tsaneva, G., Gospodinova, E., Gospodinov, M. and Cheshmedzhiev, K. “Portable sensor system for registration, processing and mathematical analysis of PPG signals,” Appl. Sci., vol. 10, no. 3, p. 22, (2020).
  • Sabeti, E., Reamaroon, N., Mathis, M., Gryak, J., Sjoding, M., and Najarian, K. “Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry,” Informatics Med. Unlocked, vol. 16, no. August, p. 100222, (2019).
  • Khreis, S., Ge, D., Rahman, H. A. and Carrault, G. “Breathing Rate Estimation Using Kalman Smoother with Electrocardiogram and Photoplethysmogram,” IEEE Trans. Biomed. Eng., vol. 67, no. 3, pp. 893–904, (2020).
  • Upadhyay, A., Singh, M., and Yadav, V. K., “Improvised number identification using SVM and random forest classifiers,” Journal of Information and Optimization Sciences, vol. 41, no. 2, pp. 387–394, (2020).
  • Singh, N., and D. Virmani, “Computational method to prove efficacy of datasets,” Journal of Information and Optimization Sciences, vol. 42, no. 1, pp. 211–233, (2021).

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