Continuous and Unconstrained Vital Signs Monitoring with Ballistocardiogram Sensors in Headrest Position Ibrahim Sadek, Jit Biswas, Bessam Abdulrazak, Zhang Haihong, Mounir Mokhtari Image & Pervasive Access Lab (IPAL), CNRS UMI 2955, Singapore.
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Introduction • Sleep disordered breathing is one of the most common clinical disorders. • Obstructive sleep apnea (OSA) is expected to influence approximately 14% of men and 5% of women in the general population [1]. • Unobtrusive and long-term monitoring can provide early diagnosis and preventive healthcare. • One possible way to monitor patients with OSA is the ballistocardiography (BCG).
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Fig.1. Typical BCG signal, IJK complex represents the ventricular ejection.
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Problem Definition • Increased need for continuous monitoring of vital signs for days and weeks under normal life conditions (Not possible in hospitals). • Increased number of elderly living alone in their homes. • Increased need for health care providers to measure vital signs of their clients. • Current methods to monitor sleep disorders such as polysomnography is bulky and expensive. Fig.2. Typical polysomnography monitoring. http://www.pq.poumon.ca/diseases-maladies/apnea-apnee/
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Objective • Heart rate estimation from human subjects resting in a massage chair through a microbend fiber optic sensor (FOS) embedded in the headrest of the chair using maximal overlap discrete wavelet transform (MODWT).
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Fig.3. Basic microbend fiber optic sensor.
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Methodology Part I: Experimental Setup and Data Collection
Fig.4. Unobtrusive remote monitoring of vital signs. Friday, February 24, 2017
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Methodology Part I: Experimental Setup and Data Collection
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Fig.5. BCG signal with its reference ECG signal, J-Peaks (dominant BCG peaks) represent heart beats as similar to R-Peaks in ECG.
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Methodology Part II: Maximal Overlap Discrete Wavelet Transform • The MODWT decomposes a finite time signal 𝑈𝑡 into wavelet coefficients (𝑊𝑘,𝑡 ) and approximation coefficients (𝑉𝑘,𝑡 ); 𝑘 denotes the decomposition level, and 𝑡 denotes the time, where 𝑘 = {1, . . . , 𝐾}, 𝑡 = {1, . . . , 𝑁 − 1}, 𝐾 represents the number of scales, and N denotes the number of time points [2]. 𝐿𝑘 −1
𝑊𝑘,𝑡 = ℎ𝑘,𝑙 . 𝑈𝑡 −𝑙 𝑚𝑜𝑑 𝑁 𝑙=0 𝐿𝑘 −1
𝑉𝑘,𝑡 = 𝑔𝑘,𝑙 . 𝑈𝑡 −𝑙 𝑚𝑜𝑑 𝑁 𝑙=0
• The length of equivalent MODWT wavelet (ℎ𝑘,𝑙 ), and approximation (𝑔𝑘,𝑙 ) filters are defined such as:
𝐿𝑘 = 2𝑘 − 1 𝐿 − 1 + 1 Friday, February 24, 2017
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Methodology Part III: Heart Rate Estimation
Fig.6. The flowchart of the proposed heart rate estimation method; BM: Body Movement, RR: Respiratory Rate, and MRA: Multiresolution Analysis. Friday, February 24, 2017
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Methodology Part III: Heart Rate Estimation - Decomposition
Fig.7. Symlet-8 MODWT multiresolution decomposition of a BCG signal. Friday, February 24, 2017
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Results and Discussion • The heart rate (HR) is estimated in beats per minutes (bpm) for ECG and BCG signals. • The average and standard deviation of the mean absolute error (MAE) are computed across all subjects to check the quality of the proposed approach regarding HR detection • A comparison between the CEEMDAN algorithm [3, 4] and the proposed MODWT multiresolution analysis is implemented to examine the performance of each method. • The CEEMDAN is applied with a noise standard deviation of 0.35, an ensemble size of 100, and a maximum number of siftings of 30.
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Results and Discussion Cont.
MODWT CEEMDAN
MAE (Average) 7.31 6.81
MAE (Std) 1.60 1.15
Run Time (Sec) 0.04 20
Table.1. The mean and Std of error for MODWT and CEEMDAN, and the run time over 10 seconds signal.
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Results and Discussion Cont.
Fig.8. Box plots of the average MAE for both CEEMDAN and MODWT methods. Friday, February 24, 2017
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Results and Discussion Cont.
Fig.9. BCG signal with the 4th level smooth coefficient during a massage session. Friday, February 24, 2017
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Conclusion and Future Work • Despite the unstable environment employed to collect BCG signals, the MODWT managed to deal with motion artifacts caused by body movements and vibrations of the massage chair during relief therapy with reasonably good results. • The MODWT is much faster than the CEEMDAN algorithm. Thus, it is an appropriate tool for real-time vital signs monitoring. • More analysis is required on BCG signals to accurately detect the heart rate on a closer beat to beat level. • Currently, the FOS mat is used in clinical study to assess the performance of the mat to diagnose patients with OSA syndrome in a collaboration with Khoo Teck Puat Hospital Singapore – KTPH, Singapore.
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References 1. R. J. Kimoff, “88 - obstructive sleep apnea,” in Murray and Nadel’s Textbook of Respiratory Medicine (Sixth Edition), V. C. Broaddus, R. J. Mason, J. D. Ernst, T. E. King, S. C. Lazarus, J. F. Murray, J. A. Nadel, A. S. Slutsky, and M. B. Gotway, Eds. Philadelphia: W.B. Saunders, 2016, pp. 1552 – 1568.e9. 2. D. B. Percival and A. T. Walden, Wavelet methods for time series analysis. Cambridge university press, 2006, vol. 4. 3. I. Sadek, J. Biswas, V. F. S. Fook, and M. Mokhtari, “Automatic heart rate detection from fbg sensors using sensor fusion and enhanced empirical mode decomposition,” in 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Dec 2015, pp. 349–353. 4. I. Sadek, J. Biswas, Z. Yongwei, Z. Haihong, J. Maniyeri, C. Zhihao, T. J. Teng, N. S. Huat, and M. Mokhtari, “Sensor data quality processing for vital signs with opportunistic ambient sensing,” in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug 2016, pp. 2484–2487.
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Thanks for listening
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Questions
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