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系統識別號 U0026-0502202001043700
論文名稱(中文) 高齡者心房顫動偵測之穿戴裝置設計與使用性研究
論文名稱(英文) Wearable Device Design and Usability Evaluation for Detecting Atrial Fibrillation of Elders
校院名稱 成功大學
系所名稱(中) 工業設計學系
系所名稱(英) Department of Industrial Design
學年度 108
學期 1
出版年 109
研究生(中文) 朱玉玲
研究生(英文) Yu-ling Chu
學號 P36064085
學位類別 碩士
語文別 中文
論文頁數 63頁
口試委員 指導教授-林彥呈
指導教授-陳建旭
口試委員-楊智鈞
中文關鍵字 穿戴式裝置  LSTM  心房顫動  使用性  高齡者 
英文關鍵字 Atrial fibrillation  Usability  Elder  Earlobe-clip heart rate monitor  Wearable device  LSTM 
學科別分類
中文摘要 隨著人口高齡化,高齡者的健康監測的需求逐漸受到重視,其中心臟監測為被證實能夠有效預防及維持健康的措施。心房顫動為高齡者常見的心臟疾病之一,隨著人口高齡化患病人數不斷攀升,預估2050年影響全球600萬至1200萬人。心房顫動容易導致心悸、暈眩等症狀,更會引發中風、心臟衰竭等併發症,然而心房顫動有時無症狀且偶發性的特性使病患不容易察覺,現有的檢查方式也無法達到長期監測。近年來人工智慧在醫療領域有越來越多的應用,例如疾病的判斷、偵測等,但這些研究多針對技術層面及臨床的證據,較少與硬體整合;而現今用於健康醫療的穿戴式裝置蓬勃發展,多數研究也主要針對技術層面,少有研究考慮到高齡者的使用性。基於以上幾點,本研究將設計一款耳部心律穿戴式裝置,經過文獻探討,選擇最合適的測量部位,並結合人工智慧長短期記憶(LSTM)判別心房顫動的的發生。本研究的裝置將透過單組前後測實驗測量使用時的錯誤率與時間,並透過改良後的整體評估用性問卷(Post-Study System Usability Questionnaire, PSSUQ)及半結構式訪談評估使用性與接受度,根據實驗結果給予改良,以提高裝置對高齡者的使用性。本研究將結合硬體穿戴式裝置及軟體人工智能判別,並考慮高齡者的使用性,設計一款高度可穿性的心率監測裝置,實驗結果可供未來高齡者穿戴式裝置的研究參考。
英文摘要 Atrial fibrillation of elders has become a serious problem leading to death in the world, which has raised the importance of heart rate monitoring. However, many of the studies focus on technical levels and clinical evidence, only a few studies pay attention to integrating hardware and software and the usability of elders. The objectives of this study are to design a new heart rhythm monitor necklace, which is easier to wear and can effectively detect heart rhythm; use long-short term memory (LSTM) to distinguish atrial fibrillation; and conduct a usability experiment which measures time and error rate of operating our prototype. The post study system usability questionnaire (PSSUQ) and a semi-structured interview are adopted to explore elders’ demands and difficulties. This work not only designs a new style wearable device for elders but also explores their demands and difficulties. Our design is expected to provide a better interaction between the user and the instrument, and help elders live healthier.
論文目次 摘要 ...............ii
SUMMARY ..............ii
ACKNOWLEDGEMENTS ...........iv
TABLE OF CONTENTS ............ v
LIST OF TABLES ..............vii
LIST OF FIGURES............viii
CHAPTER 1 INTRODUCTION........... 1
1.1 Background.............. 1
1.1.1 Elderly population and heart disease........ 1
1.1.2 Heart monitoring wearable device ....... 2
1.1.3 Artificial intelligence and atrial fibrillation ....... 3
1.2 Motivation ............. 4
1.3 Purpose .............. 5
1.4 Research Framework ........... 7
CHAPTER 2 LITERATURE REVIEW.......... 9
2.1 Atrial Fibrillation (AF) .......... 10
2.1.1 Treatments of AF.......... 11
2.1.2 The detection of AF.......... 13
2.2 Photoplethysmography, PPG......... 14
2.2.1 Principle of PPG........... 14
2.2.2 Measurement sites of PPG.......... 15
2.3 Artificial Intelligence and AF Detection ....... 16
2.4 Elderly’s Usability........... 23
2.4.1 Wearable devices and the elderly....... 23
2.4.2 Evaluation of elderly’s wearable device....... 25
CHAPTER 3 METHODOLOGY.......... 29
3.1 Hardware ............. 29
3.2 LSTM Model Building .......... 31
3.3 Usability Study ............ 32
3.3.1 Pre-processing ............ 33
3.3.2 Usability evaluation of wearable device....... 35
CHAPTER 4 RESULTS............ 37
4.1 LSTM Model ............. 37
4.1.1 Data cleaning............ 37
4.1.2 LSTM model ............ 38
4.2 Usability Test............. 41
4.3 Device Design and Improvement .......... 45
CHAPTER 5 DISCUSSION ........... 48
5.1 LSTM Model ............. 48
5.2 Usability .............. 48
CHAPTER 6 CONCULSION........... 51
REFERENCES.............. 54
參考文獻 Abbate, S., Avvenuti, M., & Light, J. (2014). Usability Study of a Wireless Monitoring System among Alzheimer's Disease Elderly Population. Int J Telemed Appl, 2014, 617495. doi:10.1155/2014/617495
Alexander, G. L., Wakefield, B. J., Rantz, M., Skubic, M., Aud, M. A., Erdelez, S., & Ghenaimi, S. A. (2011). Passive sensor technology interface to assess elder activity in independent living. Nurs Res, 60(5), 318-325. doi:10.1097/NNR.0b013e318225f3e1
Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiol Meas, 28(3), R1-39. doi:10.1088/0967-3334/28/3/R01
Allen, J., & Murray, A. (2004). Effects of filtering on multi-site photoplethysmography pulse waveform characteristics. Paper presented at the Computers in Cardiology, Chicago, IL, USA,.
Asgari, S., & Mehrnia, A. (2017). A novel low-complexity digital filter design for wearable ECG devices. PLoS One, 12(4), e0175139. doi:10.1371/journal.pone.0175139
Atrial Fibrillation (Afib). (2019, 04/10/2018). Retrieved from https://my.clevelandclinic.org/health/diseases/16765-atrial-fibrillation-afib
Barrios-Muriel, J., Sanchez, F. R., Alonso, F. J., & Salgado, D. R. (2019). Design of Semirigid Wearable Devices Based on Skin Strain Analysis. Journal of Biomechanical Engineering-Transactions of the Asme, 141(2), 9. doi:10.1115/1.4040250
Beniczky, S., Conradsen, I., Henning, O., Fabricius, M., & Wolf, P. (2018). Automated real-time detection of tonic-clonic seizures using a wearable EMG device. Neurology, 90(5), E428-+. doi:10.1212/wnl.0000000000004893
Benjamin A. Steinberg, M., MHS, & Jonathan P. Piccini, M., MHS. (2018). Screening for Atrial Fibrillation With a Wearable Device. JAMA July 10, 2018, Volume 320, Number 2.
Biswas, D., Everson, L., Liu, M., Panwar, M., Verhoef, B. E., Patki, S., . . . Van Helleputte, N. (2019). CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment. IEEE Trans Biomed Circuits Syst, 13(2), 282-291. doi:10.1109/TBCAS.2019.2892297
Burgdorf, A., Guthe, I., Jovanovic, M., Kutafina, E., Kohlschein, C., Bitsch, J. A., & Jonas, S. M. (2018). The mobile sleep lab app: An open-source framework for mobile sleep assessment based on consumer-grade wearable devices. Computers in Biology and Medicine, 103, 8-16. doi:10.1016/j.compbiomed.2018.09.025
Chan, P. H., Wong, C. K., Poh, Y. C., Pun, L., Leung, W. W., Wong, Y. F., . . . Siu, C. W. (2016). Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting. J Am Heart Assoc, 5(7). doi:10.1161/JAHA.116.003428
Chokshi, N. P., Adusumalli, S., Small, D. S., Morris, A., Feingold, J., Ha, Y. P., . . . Patel, M. S. (2018). Loss-Framed Financial Incentives and Personalized Goal-Setting to Increase Physical Activity Among Ischemic Heart Disease Patients Using Wearable Devices: The ACTIVE REWARD Randomized Trial. Journal of the American Heart Association, 7(12), 47. doi:10.1161/jaha.118.009173
Chong, J. W., Esa, N., McManus, D. D., & Chon, K. H. (2015). Arrhythmia Discrimination Using a Smart Phone. IEEE Journal of Biomedical and Health Informatics, 19(3), 815-824. doi:10.1109/JBHI.2015.2418195
Costa, A., Rincon, J. A., Carrascosa, C., Julian, V., & Novais, P. (2019). Emotions detection on an ambient intelligent system using wearable devices. Future Generation Computer Systems-the International Journal of Escience, 92, 479-489. doi:10.1016/j.future.2018.03.038
Dublin, S., Anderson, M. L., Haneuse, S. J., Heckbert, S. R., Crane, P. K., Breitner, J. C., . . . Larson, E. B. (2011). Atrial fibrillation and risk of dementia: a prospective cohort study. J Am Geriatr Soc, 59(8), 1369-1375. doi:10.1111/j.1532-5415.2011.03508.x
Eerikainen, L. M., Bonomi, A. G., Schipper, F., Dekker, L. R. C., Vullings, R., de Morree, H. M., & Aarts, R. M. (2018). Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions. Physiol Meas, 39(8), 084001. doi:10.1088/1361-6579/aad2c0
Faust, O., Shenfield, A., Kareem, M., San, T. R., Fujita, H., & Acharya, U. R. (2018). Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput Biol Med, 102, 327-335. doi:10.1016/j.compbiomed.2018.07.001
Fleddermann, A., Eckert, R., Muskala, P., Hayes, C., Magalski, A., & Main, M. L. (2019). Efficacy of Direct Acting Oral Anticoagulant Drugs in Treatment of Left Atrial Appendage Thrombus in Patients With Atrial Fibrillation. Am J Cardiol, 123(1), 57-62. doi:10.1016/j.amjcard.2018.09.026
Ghamari, M. (2018). A review on wearable photoplethysmography sensors and their potential future applications in health care. International Journal of Biosensors & Bioelectronics, 4(4). doi:10.15406/ijbsbe.2018.04.00125
Gladstone, D. J., Spring, M., Dorian, P., Panzov, V., Thorpe, K. E., Hall, J., . . . Coordinators. (2014). Atrial fibrillation in patients with cryptogenic stroke. N Engl J Med, 370(26), 2467-2477. doi:10.1056/NEJMoa1311376
Gonzalez, R., Manzo, A., Delgado, J., Padilla, J. M., Trenor, B., & Saiz, J. (2008). A computer based photoplethysmographic vascular analyzer through derivatives. Paper presented at the Computers in Cardiology, Bologna.
Guastello, S. J. (2006). Human Factors Engineering and Ergonomics: A System Approach. New York, USA: Lawrence Erlbaum Associates.
Halcox, J. P. J., Wareham, K., Cardew, A., Gilmore, M., Barry, J. P., Phillips, C., & Gravenor, M. B. (2017). Assessment of Remote Heart Rhythm Sampling Using the AliveCor Heart Monitor to Screen for Atrial Fibrillation: The REHEARSE-AF Study. Circulation, 136(19), 1784-1794. doi:10.1161/CIRCULATIONAHA.117.030583
Hammond-Haley, M., Providência, R., & Lambiase, P. D. (2018). Temporal patternepisode duration-based classification of atrial fibrillation as paroxysmal vs. persistent is it time to develop a more integrated prognostic score to optimize management. EP Europace, 20(3), 288-298. doi:10.1093/europace/eux178
Harju, J., Tarniceriu, A., Parak, J., Vehkaoja, A., Yli-Hankala, A., & Korhonen, I. (2018). Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation. Physiol Meas, 39(6), 065007. doi:10.1088/1361-6579/aac9a9
Hertzman, A. B. (1938). THE BLOOD SUPPLY OF VARIOUS SKIN AREAS AS ESTIMATED BY THE PHOTOELECTRIC PLETHYSMOGRAPH. American Journal of Physiology-Legacy Content, 124(2), 328-340. doi:10.1152/ajplegacy.1938.124.2.328
Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen netzen. Master’s thesis, Institut fur Informatik,Technische Universitat, Munchen.
Hochreiter, S., & Schmidhuber, J. u. (1997). Long short-term Memory Neural Computation, 9(8), 1735-1780.
Huei, Y. J., Hwang, G., & Jeong, D. (2011). Designing a wearable earlobe hook-type photoplethysmography sensor for ubiquitous healthcare monitoring application. Paper presented at the 2011 6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), Seogwipo,South Korea.
Iijima, M., Mitoma, H., Uchiyama, S., & Kitagawa, K. (2017). Long-term Monitoring Gait Analysis Using a Wearable Device in Daily Lives of Patients with Parkinson's Disease: The Efficacy of Selegiline Hydrochloride for Gait Disturbance. Front Neurol, 8, 542. doi:10.3389/fneur.2017.00542
January, C. T., Wann, L. S., Alpert, J. S., Calkins, H., Cigarroa, J. E., Cleveland, J. C., Jr., . . . American College of Cardiology/American Heart Association Task Force on Practice, G. (2014). 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol, 64(21), e1-76. doi:10.1016/j.jacc.2014.03.022
Jeff S. Healey, M. D., Stuart J. Connolly, M.D., Michael R. Gold, M.D., , Carsten W. Israel, M. D., Isabelle C. Van Gelder, M.D., , Alessandro Capucci, M. D., C.P. Lau, M.D., Eric Fain, M.D., Sean Yang, M.Sc., , Christophe Bailleul, M. D., Carlos A. Morillo, M.D., Mark Carlson, M.D., , Ellison Themeles, M. S., Elizabeth S. Kaufman, M.D., , & and Stefan H. Hohnloser, M. D., for the ASSERT Investigators*. (2012). Subclinical atrial fibrillation and the risk of stroke. The new england journal of medicine, 366:120-129.
Kekade, S., Hseieh, C. H., Islam, M. M., Atique, S., Mohammed Khalfan, A., Li, Y. C., & Abdul, S. S. (2018). The usefulness and actual use of wearable devices among the elderly population. Comput Methods Programs Biomed, 153, 137-159. doi:10.1016/j.cmpb.2017.10.008
Kos, A., Milutinovic, V., & Umek, A. (2019). Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications. Future Generation Computer Systems-the International Journal of Escience, 92, 582-592. doi:10.1016/j.future.2018.03.032
Kusmakar, S., Karmakar, C. K., Yan, B., O'Brien, T. J., Muthuganapathy, R., & Palaniswami, M. (2019). Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device. Ieee Transactions on Biomedical Engineering, 66(2), 421-432. doi:10.1109/tbme.2018.2845865
Lewis, J. (1992). Psychometric evaluation of the post-study system usability questionnaire: The PSSUQ (Vol. 2).
Lewis, J. R. (1995). IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use. International Journal of Human–Computer Interaction, 7(1), 57-78. doi:10.1080/10447319509526110
Liang, Y., Chen, Z., Ward, R., & Elgendi, M. (2018). Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors (Basel), 8(4). doi:10.3390/bios8040101
Liu, G. Y., Kong, D. Y., Hu, S. G., Yu, Q., Liu, Z., Chen, T. P., . . . Liu, Y. (2018). Smart electronic skin having gesture recognition function by LSTM neural network. Applied Physics Letters, 113(8), 084102. doi:10.1063/1.5040413
Maknickas, V., & Maknickas, A. (2017). Atrial Fibrillation Classification Using QRS Complex Features and LSTM. Computing in Cardiology, 44. doi:10.22489/CinC.2017.350-114
Mangiarotti, M., Ferrise, F., Graziosi, S., Tamburrino, F., & Bordegoni, M. (2019). A Wearable Device to Detect in Real-Time Bimanual Gestures of Basketball Players During Training Sessions. Journal of Computing and Information Science in Engineering, 19(1), 10. doi:10.1115/1.4041704
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115-133. doi:10.1007/BF02478259
Mencarini, E., Leonardi, C., Cappelletti, A., Giovanelli, D., De Angeli, A., & Zancanaro, M. (2019). Co-designing wearable devices for sports: The case study of sport climbing. International Journal of Human-Computer Studies, 124, 26-43. doi:10.1016/j.ijhcs.2018.10.005
Mendelson, Y., & Pujary, C. (2003, 17-21 Sept. 2003). Measurement site and photodetector size considerations in optimizing power consumption of a wearable reflectance pulse oximeter. Paper presented at the Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).
Meritam, P., Ryvlin, P., & Beniczky, S. (2018). User-based evaluation of applicability and usability of a wearable accelerometer device for detecting bilateral tonic-clonic seizures: A field study. Epilepsia, 59 Suppl 1, 48-52. doi:10.1111/epi.14051
Middlekauff, H. R., Stevenson, W. G., & Stevenson, L. W. (1991). Prognostic significance of atrial fibrillation in advanced heart failure. A study of 390 patients. Circulation, 84(1), 40-48.
Miyazawa, K., & Lip, G. Y. H. (2019). Risk assessment and management of atrial fibrillation patients with left atrial thrombus. Pacing Clin Electrophysiol, 42(1), 1-3. doi:10.1111/pace.13556
Morillo, C. A., Banerjee, A., Perel, P., Wood, D., & Jouven, X. (2017). Atrial fibrillation: the current epidemic. J Geriatr Cardiol, 14(3), 195-203. doi:10.11909/j.issn.1671-5411.2017.03.011
Munger, T. M., Wu, L. Q., & Shen, W. K. (2014). Atrial fibrillation. J Biomed Res, 28(1), 1-17. doi:10.7555/JBR.28.20130191
Nielsen, J. (1994). 10 Usability Heuristics for User Interface Design. Retrieved from http://www.nngroup.com/articles/ten-usability-heuristics
Nisar, S., Martinez, M. O., Endo, T., Matsuno, F., & Okamura, A. M. (2019). Effects of Different Hand-Grounding Locations on Haptic Performance With a Wearable Kinesthetic Haptic Device. Ieee Robotics and Automation Letters, 4(2), 351-358. doi:10.1109/lra.2018.2890198
Oh, S. L., Ng, E. Y. K., Tan, R. S., & Acharya, U. R. (2018). Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med, 102, 278-287. doi:10.1016/j.compbiomed.2018.06.002
Ott, A., Breteler Monique, M. B., de Bruyne Martine, C., van Harskamp, F., Grobbee Diederick, E., & Hofman, A. (1997). Atrial Fibrillation and Dementia in a Population-Based Study. Stroke, 28(2), 316-321. doi:10.1161/01.STR.28.2.316
Papagiannaki, A., Zacharaki, E. I., Kalouris, G., Kalogiannis, S., Deltouzos, K., Ellul, J., & Megalooikonomou, V. (2019). Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data. Sensors (Basel), 19(4). doi:10.3390/s19040880
Pierleoni, P., Belli, A., Palma, L., Pellegrini, M., Pernini, L., & Valenti, S. (2015). A High Reliability Wearable Device for Elderly Fall Detection. Ieee Sensors Journal, 15(8), 4544-4553. doi:10.1109/jsen.2015.2423562
Poh, M.-Z., Kim, K., Goessling, A., Swenson, N., & Picard, R. (2010). Cardiovascular Monitoring Using Earphones and a Mobile Device. IEEE Pervasive Computing, 11(4), 18-26. doi: 10.1109/MPRV.2010.91
Poh, M.-Z., Poh, Y. c., chan, P.-h., Wong, c.-K., Pun, l., leung, W. W.-c., . . . siu, c.-W. (2018). Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms. Heart, 104, 1921–1928. doi:10.1136/heartjnl-2018-313147
Poh, M.-Z., Swenson, N. C., & Picard, R. W. (2010). Motion-tolerant magnetic earring sensor and wireless earpiece for wearable photoplethysmography. IEEE Transactions on Information Technology in Biomedicine, 14(3), 786-794. doi:10.1109/TITB.2010.2042607
Prawiro, E., Chou, N. K., Lee, M. W., & Lin, Y. H. (2019). A Wearable System That Detects Posture and Heart Rate Designing an integrated device with multiparameter measurements for better health care. Ieee Consumer Electronics Magazine, 8(2), 78-83. doi:10.1109/mce.2018.2880829
Ross, H. M., & Richards, L. (2017). Wearable Devices Can Improve Quality of Life in Chronic Heart Failure. Journal of Cardiac Failure, 23(8), S9-S9. doi:10.1016/j.cardfail.2017.07.020
Roy, D., Talajic, M., Nattel, S., Wyse, D. G., Dorian, P., Lee, K. L., . . . Waldo, A. L. (2008). Rhythm control versus rate control for atrial fibrillation and heart failure. The new england journal of medicine, 358(25), 2667-2677. doi:10.1056/NEJMoa0708789
Sanna, T., Diener, H. C., Passman, R. S., Di Lazzaro, V., Bernstein, R. A., Morillo, C. A., . . . Investigators, C. A. (2014). Cryptogenic stroke and underlying atrial fibrillation. N Engl J Med, 370(26), 2478-2486. doi:10.1056/NEJMoa1313600
Shamir, M., Eidelman, L. A., Floman, Y., Kaplan, L., & Pizov, R. (1999). Pulse oximetry plethysmographic waveform during changes in blood volume. British Journal of Anaesthesia, 82(2), 178-181. doi:10.1093/bja/82.2.178
Shan, S.-M., Tang, S.-C., Huang, P.-W., Lin, Y.-M., Huang, W.-H., Lai, D.-M., & Wu, A.-Y. A. (2016). Reliable PPG­based algorithm in atrial fibrillation Decetion. Paper presented at the IEEE Biomedical Circuits and Systems Conference, Shanghai,China.
Shin, K., Kim, Y., Bae, S., Park, K., & Kim, S. (2009). A novel headset with a transmissive PPG sensor for heart rate measurement. Paper presented at the 13th International Conference on Biomedical Engineering Berlin, Heidelberg.
Stahrenberg, R., Weber-Kruger, M., Seegers, J., Edelmann, F., Lahno, R., Haase, B., . . . Wachter, R. (2010). Enhanced detection of paroxysmal atrial fibrillation by early and prolonged continuous holter monitoring in patients with cerebral ischemia presenting in sinus rhythm. Stroke, 41(12), 2884-2888. doi:10.1161/STROKEAHA.110.591958
Steinhubl, S. R., Mehta, R. R., Ebner, G. S., Ballesteros, M. M., Waalen, J., Steinberg, G., . . . Topol, E. J. (2016). Rationale and design of a home-based trial using wearable sensors to detect asymptomatic atrial fibrillation in a targeted population: The mHealth Screening To Prevent Strokes (mSToPS) trial. Am Heart J, 175, 77-85. doi:10.1016/j.ahj.2016.02.011
Streur, M. (2019). Atrial Fibrillation Symptom Perception. The Journal for Nurse Practitioners, 15(1), 60-64. doi:10.1016/j.nurpra.2018.08.015
Sujadevi, V. G., Soman, K. P., & Vinayakumar, R. (2017). Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks. Paper presented at the Intelligent Systems Technologies and Applications.
Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014). Wearable Photoplethysmographic Sensors—Past and Present. Electronics, 3(2), 282-302. doi:10.3390/electronics3020282
Tang, S. C., Huang, P. W., Hung, C. S., Shan, S. M., Lin, Y. H., Shieh, J. S., . . . Jeng, J. S. (2017). Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram. Sci Rep, 7, 45644. doi:10.1038/srep45644
Treatment Options of Atrial Fibrillation (AFib or AF). (2019, Jul 31, 2016). Retrieved from https://www.heart.org/en/health-topics/atrial-fibrillation/treatment-and-prevention-of-atrial-fibrillation/treatment-options-of-atrial-fibrillation-afib-or-af
Tsiouris, K., Pezoulas, V. C., Zervakis, M., Konitsiotis, S., Koutsouris, D. D., & Fotiadis, D. I. (2018). A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med, 99, 24-37. doi:10.1016/j.compbiomed.2018.05.019
Van Der Malsburg, C. (1986). Frank Rosenblatt: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. 245-248. doi:10.1007/978-3-642-70911-1_20
W.Rho, R., & L.Page, R. (2005). Asymptomatic Atrial Fibrillation. Progress in Cardiovascular Diseases, 48(2), 79-87. doi:https://doi.org/10.1016/j.pcad.2005.06.005
Weiss, A., Herman, T., Mirelman, A., Shiratzky, S. S., Giladi, N., Barnes, L. L., . . . Hausdorff, J. M. (2019). The transition between turning and sitting in patients with Parkinson's disease: A wearable device detects an unexpected sequence of events. Gait & Posture, 67, 224-229. doi:10.1016/j.gaitpost.2018.10.018
Whiting, S., Moreland, S., Costello, J., Colopy, G., & McCann, C. (2018). Recognising Cardiac Abnormalities in Wearable Device Photoplethysmography (PPG) with Deep Learning.
Wolf, P. A., Abbott, R. D., & Kannel, W. B. (1991). Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke, 22(8), 983-988. doi:10.1161/01.str.22.8.983
Yoshua, B., Patrice, S., & Paolo, F. (1994). Learning long-term dependencies with gradient descent. Neural Networks, IEEE, 5(2), 157–166.
Yu, H., Jia, W., Li, Z., Gong, F., Yuan, D., Zhang, H., & Sun, M. (2019). A multisource fusion framework driven by user-defined knowledge for egocentric activity recognition. EURASIP J Adv Signal Process, 2019(1), 14. doi:10.1186/s13634-019-0612-x
Zhang, X., Kou, W., Chang, E. I., Gao, H., Fan, Y., & Xu, Y. (2018). Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device. Comput Biol Med, 103, 71-81. doi:10.1016/j.compbiomed.2018.10.010
Zhang, Y., Liu, B., & Zhang, Z. (2015). Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist-type photoplethysmography. Biomedical Signal Processing and Control, 21, 119-125. doi:https://doi.org/10.1016/j.bspc.2015.05.006
Zhou, J., & Dong, T. (2018). Design of a wearable device for real-time screening of urinary tract infection and kidney disease based on smartphone. Analyst, 143(12), 2812-2818. doi:10.1039/c8an00157j
Zhou, S. Y., Ogihara, A., Nishimura, S., & Jin, Q. (2018). Analyzing the changes of health condition and social capital of elderly people using wearable devices. Health Information Science and Systems, 6, 10. doi:10.1007/s13755-018-0044-2
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