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Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare

  • Jinwei Bai
  • Li Shen
  • Huimin Sun
  • Bairong ShenEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1028)

Abstract

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.

Keywords

Wearable sensors Smartphone Physiological informatics Participatory medicine Data mining for healthcare 

Notes

Acknowledgments

This study was supported by the National Natural Science Foundation of China (NSFC) (grant nos. 31670851, 31470821, and 91530320) and National Key R&D programs of China (2016YFC1306605).

References

  1. 1.
    Nefiodow L, Nefiodow S (2014) The sixth Kondratieff: a new long wave in the global economy. ISBN 978-1-4961-4038-8. CharlestonGoogle Scholar
  2. 2.
    Hood L (2008) A personal journey of discovery: developing technology and changing biology. Annu Rev Anal Chem (Palo Alto, Calif) 1:1–43CrossRefGoogle Scholar
  3. 3.
    Auffray C, Charron D, Hood L (2010) Predictive, preventive, personalized and participatory medicine: back to the future. Genome Med 2(8):57CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Flores M, Glusman G, Brogaard K, Price ND, Hood L (2013) P4 medicine: how systems medicine will transform the healthcare sector and society. Personalized Med 10(6):565–576CrossRefGoogle Scholar
  5. 5.
    Elenko E, Underwood L, Zohar D (2015) Defining digital medicine. Nat Biotechnol 33(5):456–461CrossRefPubMedGoogle Scholar
  6. 6.
    McKenzie ED, Lim AS, Leung EC, Cole AJ, Lam AD, Eloyan A, Nirola DK, Tshering L, Thibert R, Garcia RZ et al (2017) Validation of a smartphone-based EEG among people with epilepsy: a prospective study. Sci Rep 7:45567CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Hu B, Peng H, Zhao Q, Hu B, Majoe D, Zheng F, Moore P (2015) Signal quality assessment model for wearable EEG sensor on prediction of mental stress. IEEE Trans Nanobioscience 14(5):553–561CrossRefPubMedGoogle Scholar
  8. 8.
    Zhang X, Li J, Liu Y, Zhang Z, Wang Z, Luo D, Zhou X, Zhu M, Salman W, Hu G et al (2017) Design of a fatigue detection system for high-speed trains based on driver vigilance using a wireless wearable EEG. Sensors (Basel) 17(3):E486CrossRefGoogle Scholar
  9. 9.
    Asakawa T, Muramatsu A, Hayashi T, Urata T, Taya M, Mizuno-Matsumoto Y (2014) Comparison of EEG propagation speeds under emotional stimuli on smartphone between the different anxiety states. Front Hum Neurosci 8:1006CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Jangho K, Da-Hye K, Wanjoo P, Laehyun K (2016) A wearable device for emotional recognition using facial expression and physiological response. Conf Proc IEEE Eng Med Biol Soc 2016:5765–5768Google Scholar
  11. 11.
    Baskaran V, Prescod F, Dong L (2015) A smartphone-based cloud computing tool for managing type 1 diabetes in Ontarians. Can J Diabetes 39(3):200–203CrossRefPubMedGoogle Scholar
  12. 12.
    Chouvarda I, Philip NY, Natsiavas P, Kilintzis V, Sobnath D, Kayyali R, Henriques J, Paiva RP, Raptopoulos A, Chetelat O et al (2014) WELCOME – innovative integrated care platform using wearable sensing and smart cloud computing for COPD patients with comorbidities. Conf Proc IEEE Eng Med Biol Soc 2014:3180–3183PubMedGoogle Scholar
  13. 13.
    Yu C, Shen B (2016) XML, ontologies, and their clinical applications. Adv Exp Med Biol 939:259–287CrossRefPubMedGoogle Scholar
  14. 14.
    Rubin DL, Shah NH, Noy NF (2008) Biomedical ontologies: a functional perspective. Brief Bioinform 9(1):75–90CrossRefPubMedGoogle Scholar
  15. 15.
    Elayavilli RK, Liu H (2016) Ion Channel Electro Physiology Ontology (ICEPO) – a case study of text mining assisted ontology development. AMIA Joint Summits Transl Sci Proc AMIA Joint Summits Transl Sci 2016:42–51Google Scholar
  16. 16.
    Gibaud B, Forestier G, Benoit-Cattin H, Cervenansky F, Clarysse P, Friboulet D, Gaignard A, Hugonnard P, Lartizien C, Liebgott H et al (2014) OntoVIP: an ontology for the annotation of object models used for medical image simulation. J Biomed Inform 52:279–292CrossRefPubMedGoogle Scholar
  17. 17.
    Cook DL, Neal ML, Bookstein FL, Gennari JH (2013) Ontology of physics for biology: representing physical dependencies as a basis for biological processes. J Biomed Semant 4(1):41CrossRefGoogle Scholar
  18. 18.
    Sahoo SS, Lhatoo SD, Gupta DK, Cui L, Zhao M, Jayapandian C, Bozorgi A, Zhang GQ (2014) Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care. J Am Med Inform Assoc AMIA 21(1):82–89CrossRefGoogle Scholar
  19. 19.
    Gundel M, Younesi E, Malhotra A, Wang J, Li H, Zhang B, de Bono B, Mevissen HT, Hofmann-Apitius M (2013) HuPSON: the human physiology simulation ontology. J Biomed Semant 4(1):35CrossRefGoogle Scholar
  20. 20.
    Hoehndorf R, Harris MA, Herre H, Rustici G, Gkoutos GV (2012) Semantic integration of physiology phenotypes with an application to the cellular phenotype ontology. Bioinforma (Oxford, England) 28(13):1783–1789CrossRefGoogle Scholar
  21. 21.
    Tinnakornsrisuphap T, Billo RE (2015) An interoperable system for automated diagnosis of cardiac abnormalities from electrocardiogram data. IEEE J Biomed Health Inform 19(2):493–500CrossRefPubMedGoogle Scholar
  22. 22.
    Bigdely-Shamlo N, Cockfield J, Makeig S, Rognon T, La Valle C, Miyakoshi M, Robbins KA (2016) Hierarchical Event Descriptors (HED): semi-structured tagging for real-world events in large-scale EEG. Front Neuroinform 10:42PubMedPubMedCentralGoogle Scholar
  23. 23.
    Li H, Wu J, Gao Y, Shi Y (2016) Examining individuals’ adoption of healthcare wearable devices: an empirical study from privacy calculus perspective. Int J Med Inform 88:8–17CrossRefPubMedGoogle Scholar
  24. 24.
    McCarthy M (2016) Federal privacy rules offer scant protection for users of health apps and wearable devices. BMJ (Clinical Res Ed) 354:i4115Google Scholar
  25. 25.
    Safavi S, Shukur Z (2014) Conceptual privacy framework for health information on wearable device. PLoS One 9(12):e114306CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Wu E, Torous J, Hardaway R, Gutheil T (2017) Confidentiality and privacy for smartphone applications in child and adolescent psychiatry: unmet needs and practical solutions. Child Adolesc Psychiatr Clin N Am 26(1):117–124CrossRefPubMedGoogle Scholar
  27. 27.
    Zhu H, Liu X, Lu R, Li H (2017) Efficient and privacy-preserving online medical prediagnosis framework using nonlinear SVM. IEEE J Biomed Health Inform 21(3):838–850CrossRefPubMedGoogle Scholar
  28. 28.
    Bayasi N, Tekeste T, Saleh H, Mohammad B, Khandoker A, Ismail M (2016) Low-power ECG-based processor for predicting ventricular arrhythmia. IEEE Trans Very Large Scale Integr (VLSI) Syst 24(5):1962–1974CrossRefGoogle Scholar
  29. 29.
    Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I (2012) DEAP: a database for emotion analysis using physiological signals. IEEE Trans Affect Comput 3(1):18–31CrossRefGoogle Scholar
  30. 30.
    Kim YG, Shin D, Park MY, Lee S, Jeon MS, Yoon D, Park RW (2017) ECG-ViEW II, a freely accessible electrocardiogram database. PLoS One 12(4):e0176222CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Jenkins JM, Jenkins RE (2003) Arrhythmia database for algorithm testing: surface leads plus intracardiac leads for validation. J Electrocardiol 36:157–161CrossRefPubMedGoogle Scholar
  32. 32.
    Mukkamala R, Moody GB, Mark RG (2001) Introduction of computational models to PhysioNet. Comput Cardiol 28:77–80PubMedGoogle Scholar
  33. 33.
    Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):E215–E220CrossRefPubMedGoogle Scholar
  34. 34.
    Moody GB, Mark RG, Goldberger AL (2000) PhysioNet: a research resource for studies of complex physiologic and biomedical signals. Comput Cardiol 27:179–182PubMedGoogle Scholar
  35. 35.
    Moody GB, Mark RG, Goldberger AL (2001) PhysioNet: a web-based resource for the study of physiologic signals. IEEE Eng Med Biol Mag 20(3):70–75CrossRefPubMedGoogle Scholar
  36. 36.
    Costa M, Moody GB, Henry I, Goldberger AL (2003) PhysioNet: an NIH research resource for complex signals. J Electrocardiol 36(Suppl):139–144CrossRefPubMedGoogle Scholar
  37. 37.
    Obeid I, Picone J (2016) The Temple University Hospital EEG data corpus. Front Neurosci 10:196CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Devuyst S, Dutoit T, Stenuit P, Kerkhofs M (2011) Automatic sleep spindles detection – overview and development of a standard proposal assessment method. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference 2011:1713–1716Google Scholar
  39. 39.
    Tsanas A, Clifford GD (2015) Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing. Front Hum Neurosci 9:15CrossRefGoogle Scholar
  40. 40.
    Khandelwal S, Wickstrom N (2017) Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database. Gait Posture 51:84–90CrossRefPubMedGoogle Scholar
  41. 41.
    Balazia M, Plataniotis KN (2017) Human gait recognition from motion capture data in signature poses. Iet Biom 6(2):129–137CrossRefGoogle Scholar
  42. 42.
    Herrera LJ, Fernandes CM, Mora AM, Migotina D, Largo R, Guillen A, Rosa AC (2013) Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification. Int J Neural Syst 23(3):1350012CrossRefPubMedGoogle Scholar
  43. 43.
    Romhilt DW, Estes EH Jr (1968) A point-score system for the ECG diagnosis of left ventricular hypertrophy. Am Heart J 75(6):752–758CrossRefPubMedGoogle Scholar
  44. 44.
    Skjaeggestad O, Kierulf P (1971) A simplified point score system for the electrocardiographic diagnosis of left ventricular hypertrophy. Acta Med Scand 190(6):527–529PubMedGoogle Scholar
  45. 45.
    Sivaraks H, Ratanamahatana CA (2015) Robust and accurate anomaly detection in ECG artifacts using time series motif discovery. Comput Math Methods Med 2015:453214CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Takahashi T, Cho RY, Mizuno T, Kikuchi M, Murata T, Takahashi K, Wada Y (2010) Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. NeuroImage 51(1):173–182CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Abbasi H, Bennet L, Gunn AJ, Unsworth CP (2017) Robust wavelet stabilized ‘Footprints of Uncertainty’ for fuzzy system classifiers to automatically detect sharp waves in the EEG after hypoxia ischemia. Int J Neural Syst 27(3):1650051CrossRefPubMedGoogle Scholar
  48. 48.
    Kim J, Hyub H, Yoon SZ, Choi HJ, Kim KM, Park SH (2014) Analysis of EEG to quantify depth of anesthesia using hidden Markov model. Conf Proc IEEE Eng Med Biol Soc 2014:4575–4578PubMedGoogle Scholar
  49. 49.
    Amin HU, Malik AS, Ahmad RF, Badruddin N, Kamel N, Hussain M, Chooi WT (2015) Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australas Phys Eng Sci Med 38(1):139–149CrossRefPubMedGoogle Scholar
  50. 50.
    Mahadevan A, Mugler DH, Acharya S (2008) Adaptive filtering of ballistocardiogram artifact from EEG signals using the dilated discrete Hermite transform. Conf Proc IEEE Eng Med Biol Soc 2008:2630–2633PubMedGoogle Scholar
  51. 51.
    Oweis RJ, Abdulhay EW (2011) Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed Eng Online 10:38CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Poh KK, Marziliano P (2007) Analysis of neonatal EEG signals using Stockwell transform. Conf Proc IEEE Eng Med Biol Soc 2007:594–597PubMedGoogle Scholar
  53. 53.
    Thuraisingham RA, Tran Y, Craig A, Nguyen H (2012) Frequency analysis of eyes open and eyes closed EEG signals using the Hilbert-Huang transform. Conf Proc IEEE Eng Med Biol Soc 2012:2865–2868PubMedGoogle Scholar
  54. 54.
    Vidal F, Burle B, Spieser L, Carbonnell L, Meckler C, Casini L, Hasbroucq T (2015) Linking EEG signals, brain functions and mental operations: advantages of the Laplacian transformation. Int J Psychophysiol Off J Int Organ Psychophysiol 97(3):221–232Google Scholar
  55. 55.
    Chan NY, Choy CC (2017) Screening for atrial fibrillation in 13 122 Hong Kong citizens with smartphone electrocardiogram. Heart 103(1):24–31CrossRefPubMedGoogle Scholar
  56. 56.
    Jain SK, Bhaumik B (2017) An energy efficient ECG signal processor detecting cardiovascular diseases on smartphone. IEEE Trans Biomed Circ Syst 11(2):314–323CrossRefGoogle Scholar
  57. 57.
    Son J, Park J, Oh H, Bhuiyan MZA, Hur J, Kang K (2017) Privacy-preserving electrocardiogram monitoring for intelligent arrhythmia detection. Sensors (Basel) 17(6):E1360CrossRefGoogle Scholar
  58. 58.
    Muhlestein JB, Le V, Albert D, Moreno FL, Anderson JL, Yanowitz F, Vranian RB, Barsness GW, Bethea CF, Severance HW et al (2015) Smartphone ECG for evaluation of STEMI: results of the ST LEUIS pilot study. J Electrocardiol 48(2):249–259CrossRefPubMedGoogle Scholar
  59. 59.
    Yang H, Fayad A, Chaput A, Oake S, Chan AD, Crossan ML (2017) Postoperative real-time electrocardiography monitoring detects myocardial ischemia: a case report. Canadian J Anaesth=J Can Anesth 64(4):411–415CrossRefGoogle Scholar
  60. 60.
    Melillo P, Castaldo R, Sannino G, Orrico A, de Pietro G, Pecchia L (2015) Wearable technology and ECG processing for fall risk assessment, prevention and detection. Conf Proc IEEE Eng Med Biol Soc 2015:7740–7743PubMedGoogle Scholar
  61. 61.
    Peritz DC, Howard A, Ciocca M, Chung EH (2015) Smartphone ECG aids real time diagnosis of palpitations in the competitive college athlete. J Electrocardiol 48(5):896–899CrossRefPubMedGoogle Scholar
  62. 62.
    Karlen W, Mattiussi C, Floreano D (2009) Sleep and wake classification with ECG and respiratory effort signals. IEEE Trans Biomed Circuits Syst 3(2):71–78CrossRefPubMedGoogle Scholar
  63. 63.
    Wang Y, Chen JJ, Li QH, Wang HY, Liu GQ, Jing Q, Shen BR (2011) Identifying novel prostate cancer associated pathways based on integrative microarray data analysis. Comput Biol Chem 35(3):151–158CrossRefPubMedGoogle Scholar
  64. 64.
    Tang YF, Yan WY, Chen JJ, Luo C, Kaipia A, Shen BR (2013) Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer. BMC Syst Biol 7:S6CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Hu YF, Li JQ, Yan WY, Chen JJ, Li Y, Hu G, Shen BR (2013) Identifying novel glioma associated pathways based on systems biology level meta-analysis. BMC Syst Biol 7:S9CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Chen JJ, Zhang DQ, Yan WY, Yang DR, Shen BR (2013) Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the next-generation sequencing era. Biomed Res Int 2013:901578PubMedPubMedCentralGoogle Scholar
  67. 67.
    Chen JJ, Sun MM, Shen BR (2015) Deciphering oncogenic drivers: from single genes to integrated pathways. Brief Bioinform 16(3):413–428CrossRefPubMedGoogle Scholar
  68. 68.
    Chen JJ, Wang Y, Guo DY, Shen BR (2012) A systems biology perspective on rational design of peptide vaccine against virus infections. Curr Top Med Chem 12(12):1310–1319CrossRefPubMedGoogle Scholar
  69. 69.
    Lin YX, Yuan XY, Shen BR (2016) Network-based biomedical data analysis. In: Shen B, Tang H, Jiang X (eds) Translational biomedical informatics: a precision medicine perspective, vol 939. Springer, Singapore, pp 309–332CrossRefGoogle Scholar
  70. 70.
    Shen K, Shen L, Wang J, Jiang Z, Shen B (2015) Understanding amino acid mutations in hepatitis B virus proteins for rational design of vaccines and drugs. Adv Protein Chem Struct Biol 99:131–153CrossRefPubMedGoogle Scholar
  71. 71.
    Moss AJ, Zareba W, Benhorin J, Locati EH, Hall WJ, Robinson JL, Schwartz PJ, Towbin JA, Vincent GM, Lehmann MH (1995) ECG T-wave patterns in genetically distinct forms of the hereditary long QT syndrome. Circulation 92(10):2929–2934CrossRefPubMedGoogle Scholar
  72. 72.
    Vanninen SUM, Nikus K, Aalto-Setala K (2017) Electrocardiogram changes and atrial arrhythmias in individuals carrying sodium channel SCN5A D1275N mutation. Ann Med 49(6):496–503CrossRefPubMedGoogle Scholar
  73. 73.
    Moeller F, Groening K, Moehring J, Muhle H, Wolff S, Jansen O, Stephani U, Siniatchkin M (2014) EEG-fMRI in myoclonic astatic epilepsy (doose syndrome). Neurology 82(17):1508–1513CrossRefPubMedGoogle Scholar
  74. 74.
    Perlaki G, Orsi G, Schwarcz A, Bodi P, Plozer E, Biczo K, Aradi M, Doczi T, Komoly S, Hejjel L et al (2015) Pain-related autonomic response is modulated by the medial prefrontal cortex: an ECG-fMRI study in men. J Neurol Sci 349(1–2):202–208CrossRefPubMedGoogle Scholar
  75. 75.
    Arns M, Gordon E, Boutros NN (2017) EEG abnormalities are associated with poorer depressive symptom outcomes with escitalopram and venlafaxine-XR, but not sertraline: results from the multicenter randomized iSPOT-D study. Clin EEG Neurosci 48(1):33–40CrossRefPubMedGoogle Scholar
  76. 76.
    Lee SH, Yoon S, Kim JI, Jin SH, Chung CK (2014) Functional connectivity of resting state EEG and symptom severity in patients with post-traumatic stress disorder. Prog Neuro-Psychopharmacol Biol Psychiatry 51:51–57CrossRefGoogle Scholar
  77. 77.
    Peng Q, Schork NJ, Wilhelmsen KC, Ehlers CL (2017) Whole genome sequence association and ancestry-informed polygenic profile of EEG alpha in a native American population. Am J Med Genet Part B, Neuropsychiatr Genet Off Publ Int Soc Psychiatr Genet 174(4):435–450CrossRefGoogle Scholar
  78. 78.
    Bonanni L, Franciotti R, Nobili F, Kramberger MG, Taylor JP, Garcia-Ptacek S, Falasca NW, Fama F, Cromarty R, Onofrj M et al (2016) EEG markers of dementia with Lewy bodies: a multicenter cohort study. J Alzheim Dis JAD 54(4):1649–1657Google Scholar
  79. 79.
    Hautz WE, Krummrey G, Exadaktylos A, Hautz SC (2016) Six degrees of separation: the small world of medical education. Med Educ 50(12):1274–1279CrossRefPubMedGoogle Scholar
  80. 80.
    Wicks P, Massagli M, Frost J, Brownstein C, Okun S, Vaughan T, Bradley R, Heywood J (2010) Sharing health data for better outcomes on PatientsLikeMe. J Med Internet Res 12(2):e19CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Wang L, Fang Y, Aref D, Rathi S, Shen L, Jiang X, Wang S (2016) PALME: PAtients Like My gEnome. AMIA Joint Summits Transl Sci Proc AMIA Joint Summits Transl Sci 2016:219–224Google Scholar
  82. 82.
    Engel GL (2012) The need for a new medical model: a challenge for biomedicine. Psychodyn Psychiatry 40(3):377–396CrossRefPubMedGoogle Scholar
  83. 83.
    Collins FS, Fleming R (2017) Sound health: an NIH-Kennedy center initiative to explore music and the mind. JAMAGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Digital Department of LibrarySoochow UniversitySuzhouChina
  2. 2.Center for Systems BiologySoochow UniversitySuzhouChina
  3. 3.Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu ProvinceNanjing Forestry UniversityNanjingChina

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