近年來，個人健康照護成為越來越受人矚目的議題，進而使得市場對於無線且可穿戴之生理訊號量測裝置有大量的需求。這些裝置具有潛力能處理幾項挑戰，例如人口老齡化帶來的巨大醫療保健需求、逐步增加的醫療成本、以及醫療專業人員的短缺。在多種生理資訊之中，心臟功能是最基本的量測項目，因為心血管疾病十幾年來都是全球的十大主要死因之一，此外，隨著國家的開發程度越高，心血管疾病在致死原因的排名中就越靠前段，甚至為第一名。而根據世界衛生組織的建議，盡早發現此疾病對於後續的諮詢與藥物管理很重要，為了檢測心血管疾病，監測心電訊號即是必須執行的項目，通過分析此訊號可以得知許多心臟活動的資訊，進而應用在疾病診斷或健康照護上。正因為此訊號的重要性，開發其穿戴式量測系統是研究趨勢之一，此類系統能讓使用者隨身穿戴，使得訊號量測較不受時間與場所限制，因而被期望為提供更完善健康照護的方案。
本篇論文將以穿戴式系統為主軸，心電為應用主題，分成兩大部分探討。第一部分實現一應用於心電感測衣物之運動偽影降低系統。此系統可分成三大區塊，即心電感測衣、訊號量測裝置與訊號處理演算法。心電感測衣整合之電極與訊號傳輸線採用導電紡織材料製作，目的為提供在長期使用的情境下較好的舒適度。訊號量測裝置之主體為感測心電與運動偽影參考訊號之前端電路，以及無線傳輸用之藍芽模組。訊號處理演算法則用於辨識心電訊號中的運動偽影並降低其影響，此部分對於乾式電極尤其重要，因為乾式電極並不使用導電凝膠與黏著劑來固定其與皮膚接觸之位置，所以運動偽影的影響比起傳統氯化銀電極更加嚴重。在與現有文獻比較下，所提出之系統除完整性較高之外，也能提供較好之定量結果。
第二部分則是提出一應用於心電訊號感測前端電路之高通三角積分調變器。穿戴式裝置之電源一般由電池供應，且小型裝置才能符合穿戴式之需求，這導致電池電量難以提高，因而穿戴式裝置所面臨的挑戰之一便是功耗。為了進一步降低現有高通三角積分調變器之功耗，本論文提出一新型高通積分器，使得主要功耗來源之放大器能被重複利用，進而在相同調變器階數下降低放大器數量。此外，可變前授係數亦被採用來增加調變器的動態範圍，藉此降低前級放大器所需規格，有望能進一步降低整體類比前端電路功耗。所提出之高通三角積分調變器以TSMC 0.18μm standard CMOS 製程製造，其效能與現有文獻比較下除具有較低的功耗外，也提供較寬的動態範圍。
本論文包含一個系統與一個電路，皆是在穿戴式心電量測主題下進行探討，期望未來進一步優化設計後，對於穿戴式健康照護的發展能做出貢獻。

In recent years, personal healthcare has become increasingly popular. This popularity has led to significant demand for wireless and wearable physiological signal measurement devices. These devices have the potential to address several challenges, such as the enormous healthcare needs of an aging population, increasing healthcare costs, and the shortage of healthcare professionals. Among many types of physiological information, cardiac function is the most essential item to be monitored because cardiovascular disease has been one of the top 10 leading causes of death worldwide for more than a decade. Additionally, cardiovascular disease has a higher or even highest rank in the list of causes of death with more income in a country. According to the suggestion from World Health Organization, early detection of the disease is important for subsequent counseling and medication management. To detect cardiovascular disease, monitoring of electrocardiogram (ECG) signal is essential. By analyzing this signal, a lot of information about heart activity can be obtained, which can be used for disease diagnosis or healthcare. Because of the importance of this signal, developing wearable systems for measuring it is one of the research trends. These systems can be carried by the user easily, making the signal measurement less restricted in time and place, and thus are expected to be the solutions to provide better healthcare.
This dissertation focuses on the wearable systems used for measuring ECG signals and is divided into two main parts to discuss the above issue. The first part proposes a motion artifact reduction system for ECG measuring clothing. This system can be divided into three major blocks, namely, ECG measuring clothing, signal acquisition device, and signal processing algorithm. The electrodes and signal transmission lines of the ECG measuring clothing are made of conductive fabric materials to provide better comfort under long-term usage. The main parts of the signal acquisition device are the front-end circuit for sensing ECG and motion artifact reference signals and the Bluetooth module for wireless transmission. The signal processing algorithm is designed to identify the motion artifacts in the ECG signals and reduce their effects. This part is especially important for dry electrodes. Compared with conventional Ag/AgCl electrodes, the motion artifacts are more severe when using dry electrodes because conductive gel and adhesive are not adopted to fix the electrodes on the skin. In comparison with the existing literature, the proposed system is more complete and can provide better quantitative results.
In the second section, a high-pass sigma-delta modulator is proposed for the front-end circuit of ECG measurement. One of the challenges for wearable devices is power consumption because the power is generally supplied by batteries. The device size needs to be small to meet the requirement of the wearable application, which makes the battery power difficult to increase. To further reduce the power consumption of the existing high-pass sigma-delta modulator, this dissertation proposes a new high-pass integrator that allows the amplifier, which is the main source of power consumption, to be reused. Therefore, the number of amplifiers can be reduced with the same order of modulator. In addition, programmable feedforward coefficients are also used to increase the dynamic range of the modulator, thus reducing the specification requirement of the pre-amplifier, which is expected to further reduce the overall power consumption of the analog front-end circuit. The proposed high-pass sigma-delta modulator is fabricated in TSMC 0.18μm standard CMOS process. The measurement results show a wider dynamic range with lower power consumption compared with the existing literature.
This dissertation consists of one system and one circuit, and they are within the theme of wearable ECG measurement. It is expected that these studies will contribute to the development of wearable healthcare after further optimizing the design in the future.

[1] B. Taji, A. D. C. Chan and S. Shirmohammadi, “Effect of pressure on skin-electrode impedance in wearable biomedical measurement Devices,” IEEE Trans. Instrum. Meas., vol. 67, no. 8, pp. 1900-1912, Aug. 2018.
[2] World Health Organization, The top 10 causes of death, December 2020. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. Accessed: May 19, 2023.
[3] World Health Organization, Cardiovascular diseases (CVDs), June 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed: May 19, 2023.
[4] M. Kirst, B. Glauner and J. Ottenbacher, “Using DWT for ECG motion artifact reduction with noise-correlating signals,” Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, Aug. 2011, pp. 4804-4807.
[5] P. Maji, H. K. Mondal, A. P. Roy, S. Poddar and S. P. Mohanty, “iKardo: An Intelligent ECG Device for Automatic Critical Beat Identification for Smart Healthcare,” IEEE Trans. Consum. Electron., vol. 67, no. 4, pp. 235-243, Nov. 2021.
[6] S. Raj, “An Efficient IoT-Based Platform for Remote Real-Time Cardiac Activity Monitoring,” IEEE Trans. Consum. Electron., vol. 66, no. 2, pp. 106-114, May 2020.
[7] S. Y. Lee, P. W. Huang, M. C. Liang, J. H. Hong and J. Y. Chen, “Development of an Arrhythmia Monitoring System and Human Study,” IEEE Trans. Consum. Electron., vol. 64, no. 4, pp. 442-451, Nov. 2018.
[8] M. A. Yokus and J. S. Jur, “Fabric-based wearable dry electrodes for body surface biopotential recording,” IEEE Trans. Biomed. Eng., vol. 63, no. 2, pp. 423-430, Feb. 2016.
[9] Y. M. Chi, T. Jung and G. Cauwenberghs, “Dry-contact and noncontact biopotential electrodes: methodological review,” IEEE Rev. Biomed. Eng., vol. 3, pp. 106-119, Oct. 2010.
[10] J. G. Webster, “Reducing motion artifacts and interference in biopotential recording,” IEEE Trans. Biomed. Eng., vol. BME-31, no. 12, pp. 823-826, Dec. 1984.
[11] B. Pholpoke, T. Songthawornpong and W. Wattanapanitch, “A micropower motion artifact estimator for input dynamic range reduction in wearable ECG acquisition systems,” IEEE Trans. Biomed. Circuits Syst., vol. 13, no. 5, pp. 1021-1035, Oct. 2019.
[12] D. Berwal, V. C.R., S. Dewan, J. C.V. and M. S. Baghini, “Motion artifact removal in ambulatory ECG signal for heart rate variability analysis,” IEEE Sensors J., vol. 19, no. 24, pp. 12432-12442, Dec. 2019.
[13] N. V. Thakor and Y. -. Zhu, “Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection,” IEEE Trans. Biomed. Eng., vol. 38, no. 8, pp. 785-794, Aug. 1991.
[14] L. R. Keytel, J. H. Goedecke, T. D. Noakes, H. Hiiloskorpi, R. Laukkanen, L van der Merwe, and E. V. Lambert, “Prediction of energy expenditure from heart rate monitoring during submaximal exercise,” J Sports Sci., vol. 23, no.3, pp. 289-297, Mar. 2005.
[15] Laborde, Sylvain et al. “Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research - Recommendations for Experiment Planning, Data Analysis, and Data Reporting.” Frontiers in psychology vol. 8 213. 20 Feb. 2017.
[16] P. Kligfield, L. S. Gettes, J. J. Bailey, R. Childers, B. J. Deal, E. W. Hancock, G. V. Herpen, J. A. Kors, P. Macfarlane, D. M. Mirvis, O. Pahlm, P. Rautaharju, and G. S. Wagner, “Recommendations for the standardization and interpretation of the electrocardiogram: part I: The electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology,” Circulation, vol. 115, no. 10, pp. 1306-1324, Mar 2007.
[17] R. E. Mason and I. Likar, “A new system of multiple-lead exercise electrocardiography,” Am. Heart J., vol. 71, no. 2, pp.196-205, Feb. 1966.
[18] M. Papouchado, P. R. Walker, M. A. James and L. M. Clarke, “Fundamental differences between the standard 12-lead electrocardiograph and the modified (Mason-Likar) exercise lead system,” Eur Heart J., vol. 8, no. 7, pp. 725-733, July 1987.
[19] M. Takano and A. Ueno, “Noncontact in-bed measurements of physiological and behavioral signals using an integrated fabric-sheet sensing scheme,” IEEE J. Biomed. Health Inform., vol. 23, no. 2, pp. 618-630, March 2019.
[20] M. Takano, H. Komiya and A. Ueno, “Stability improvement and noise suppression in non-contact in-bed electrocardiogram measurement using laminated feedback electrode,” IEEE Biomed. Circuits Syst. Conf., (BioCAS), Oct. 2017, pp. 1-4.
[21] L. Leicht, B. Eilebrecht, S. Weyer, S. Leonhardt and D. Teichmann, “Closed-loop control of humidification for artifact reduction in capacitive ECG measurements,” IEEE Trans. Biomed. Circuits Syst., vol. 11, no. 2, pp. 300-313, April 2017.
[22] H. J. Baek, J. S. Kim, K. K. Kim and K. S. Park, “System for unconstrained ECG measurement on a toilet seat using capacitive coupled electrodes: The efficacy and practicality,” 30th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, Aug. 2008, pp. 2326-2328.
[23] G. Cho, K. Jeong, M. J. Paik, Y. Kwun, and M. Sung, “Performance evaluation of textile-based electrodes and motion sensors for smart clothing,” IEEE Sensors J., vol. 11, no. 12, pp. 3183–3193, Dec. 2011.
[24] R. R. Rajanna, N. Sriraam, P. R. Vittal and U. Arun, “Performance evaluation of woven conductive dry textile electrodes for continuous ECG signals acquisition,” IEEE Sensors J., vol. 20, no. 3, pp. 1573-1581, Feb. 2020.
[25] D. Pani, A. Dessì, J. F. Saenz-Cogollo, G. Barabino, B. Fraboni and A. Bonfiglio, “Fully Textile, PEDOT:PSS Based Electrodes for Wearable ECG Monitoring Systems,” IEEE Trans. Biomed. Eng., vol. 63, no. 3, pp. 540-549, Mar. 2016.
[26] A. Achilli, A. Bonfiglio and D. Pani, “Design and Characterization of Screen-Printed Textile Electrodes for ECG Monitoring,” IEEE Sensors J. vol. 18, no. 10, pp. 4097-4107, May. 2018.
[27] H. Ozkan, O. Ozhan, Y. Karadana, M. Gulcu, S. Macit and F. Husain, “A portable wearable tele-ECG monitoring system,” IEEE Trans. Instrum. Meas., vol. 69, no. 1, pp. 173-182, Jan. 2020.
[28] Y. Choi, J. Lee and S. Kong, “Driver ECG measuring system with a conductive fabric-based dry electrode,” IEEE Access, vol. 6, pp. 415-427, Oct. 2017.
[29] A. L. Goldberger et al., “Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, June 2000.
[30] M. A. Yokus, R. Foote and J. S. Jur, “Printed Stretchable Interconnects for Smart Garments: Design, Fabrication, and Characterization,” IEEE Sensors J., vol. 16, no. 22, pp. 7967-7976, Nov.15, 2016.
[31] L. J. L. Ruiz et al., “Self-Powered Cardiac Monitoring: Maintaining Vigilance With Multi-Modal Harvesting and E-Textiles,” IEEE Sensors J., vol. 21, no. 2, pp. 2263-2276, 15 Jan.15, 2021.
[32] S. Masihi et al., “Development of a Flexible Wireless ECG Monitoring Device With Dry Fabric Electrodes for Wearable Applications,” IEEE Sensors J., vol. 22, no. 12, pp. 11223-11232, 15 June.15, 2022.
[33] C. Zou, Y. Qin, C. Sun, W. Li, W. Chen, “Motion artifact removal based on periodical property for ECG monitoring with wearable systems,” Pervasive Mob. Computing., vol. 40, pp. 267-278, Sept. 2017.
[34] F. A. Ghaleb, M. B. Kamat, M. Salleh, M. F. Rohani, and S. Abd Razak, “Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter,” PLoS One, vol. 13, no. 11, Nov. 2018.
[35] H. Kim et al., “A configurable and low-power mixed signal SoC for portable ECG monitoring applications,” IEEE Trans. Biomed. Circuits Syst., vol. 8, no. 2, pp. 257-267, April 2014.
[36] D. Buxi et al., “Wireless 3-lead ECG system with on-board digital signal processing for ambulatory monitoring,” IEEE Biomed. Circuits Syst. Conf. (BioCAS), Nov. 2012, pp. 308-311. Hsinchu.
[37] X. Xu, Y. Liang, P. He, and J. Yang, “Adaptive motion artifact reduction based on empirical wavelet transform and wavelet thresholding for the noncontact ECG monitoring systems,” Sensors 19, no. 13: 2916, July 2019.
[38] Z. He and H. Min, “Skin-Induced Motion Artifact Removal for Ambulatory Electrocardiography,” IEEE Sensors J., vol. 22, no. 15, pp. 15033-15043, Aug. 2022.
[39] T. Berset, D. Geng and I. Romero, “An optimized DSP implementation of adaptive filtering and ICA for motion artifact reduction in ambulatory ECG monitoring,” Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, Sept. 2012, pp. 6496-6499. San Diego, CA.
[40] N. Van Helleputte et al., “A 345 µW multi-sensor biomedical SoC with bio-impedance, 3-channel ECG, motion artifact reduction, and integrated DSP,” IEEE J. Solid-State Circuits, vol. 50, no. 1, pp. 230-244, Jan. 2015.
[41] N. Van Helleputte, S. Kim, H. Kim, J. P. Kim, C. Van Hoof and R. F. Yazicioglu, “A 160 μA biopotential acquisition IC with fully integrated IA and motion artifact suppression,” IEEE Trans. Biomed. Circuits Syst., vol. 6, no. 6, pp. 552-561, Dec. 2012.
[42] F. A. Ghaleb, M. B. Kamat, M. Salleh, M. F. Rohani, and S. E. Hadji, “Motion Artifact Reduction Algorithm Using Sequential Adaptive Noise Filters and Estimation Methods for Mobile ECG,” International Conference of Reliable Information and Communication Technology, May 2017, pp. 116-123.
[43] F. Xiong, D. Chen, Z. Chen, and S. Dai, “Cancellation of motion artifacts in ambulatory ECG signals using TD-LMS adaptive filtering techniques,” Journal of Visual Communication and Image Representation, vol. 58, pp. 606–618, Jan. 2019.
[44] Y. Liu and M. G. Pecht, “Reduction of skin stretch induced motion artifacts in electrocardiogram monitoring using adaptive filtering,” Int. Conf. IEEE Engineering in Medicine and Biology Society, Sept. 2006, pp. 6045-6048.
[45] T. Seol, S. Lee and J. Lee, “A wearable electrocardiogram monitoring system robust to motion artifacts,” Int. SoC Design Conf. (ISOCC), Nov. 2018, pp. 241-242.
[46] P. S. Hamilton, M. G. Curley, R. M. Aimi, and C. Sae-Hau, “Comparison of methods for adaptive removal of motion artifact,” Comput. Cardiol., 2000, vol. 27, pp. 383–386.
[47] S. Kim, R. F. Yazicioglu, T. Torfs, B. Dilpreet, P. Julien and C. Van Hoof, “A 2.4µA continuous-time electrode-skin impedance measurement circuit for motion artifact monitoring in ECG acquisition systems,” Symp. VLSI Circuits, June 2010, pp. 219-220.
[48] T. Torfs, Y. Chen, H. Kim and R. F. Yazicioglu, “Noncontact ECG recording system with real time capacitance measurement for motion artifact reduction,” IEEE Trans. Biomed. Circuits Syst., vol. 8, no. 5, pp. 617-625, Oct. 2014.
[49] T. Degen and H. JÄckel, “Continuous monitoring of electrode-skin impedance mismatch during bioelectric recordings,” IEEE Trans. Biomed. Eng., vol. 55, no. 6, pp. 1711-1715, June 2008.
[50] S. Grimnes, “Impedance measurement of individual skin surface electrodes,” Med. Biol. Eng. Comput., vol. 21, no. 6, pp. 750–755, Nov. 1983.
[51] B. Taji, S. Shirmohammadi, V. Groza and I. Batkin, “Impact of skin–electrode interface on electrocardiogram measurements using conductive textile electrodes,” IEEE Trans. Instrum. Meas., vol. 63, no. 6, pp. 1412-1422, June 2014.
[52] H. H. So and K. L. Chan, “Development of QRS detection method for real-time ambulatory cardiac monitor,” in Proc. 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Oct.-Nov. 1997, pp. 289–292.
[53] G. Friesen, T. Jannett, M. Jadallah, S. Yates, S. Quint, and H. Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Trans. Biomed. Eng., vol. 37, no. 1, pp. 85–98, 1990.
[54] N. Tasneem, D. Kota, I. Mahbub, G. Mehta, K. Namuduri and A. Cedars, “A dry electrode-based ECG sensor with motion artifacts cancellation and signal analysis for heart irregularity detection,” IEEE SENSORS, Oct. 2020, pp. 1-4.
[55] Xiang An et al., “Adaptive motion artifact reduction in wearable ECG measurements using impedance pneumography signal,” Sensors 22, no. 15: 5493, July 2022.
[56] P. H. Su, K. L. Huang and S. Y. Lee, “Operational Amplifier Sharing Based High-Pass Sigma-Delta Modulator with Programmable Feedforward Coefficients for ECG Signal Acquisition,” Proc. IEEE Int. Symp. Circuits and Syst., Sevilla, Oct. 2020, pp. 1-4.
[57] S. Y. Lee, P. W. Huang, J. R. Chiou, C. Tsou, Y. Y. Liao and J. Y. Chen, “Electrocardiogram and Phonocardiogram Monitoring System for Cardiac Auscultation,” IEEE Trans. Biomed. Circuits Syst., vol. 13, no. 6, pp. 1471-1482, Dec. 2019.
[58] R. Mohan, S. Hiseni, and W. A. Serdijn, “A highly linear, sigma-delta based, sub-Hz high-pass filtered ExG readout system,” Proc. IEEE Int. Symp. Circuits and Syst., Beijing, China, May 19-23, 2013, pp. 181-184.
[59] L. S. Y. Wong, S. Hossain, A. Ta, J. Edvinsson, D. H. Rivas, and H. Nääs, “A very low-power CMOS mixed-signal IC for implantable pacemaker applications,” IEEE J. Solid-State Circuits, vol. 39, no.12, pp. 2446-2456, Dec. 2004.
[60] S. Y. Lee, Y. C. Su, M. C. Liang, Y. Y. Chen, C. H. Hsieh, C. M. Yang, H. Y. Lai, J. W. Lin, and Q. Fang, “A programmable implantable microstimulator SoC with wireless telemetry: Application in closed-loop endocardial stimulation for cardiac pacemaker,” IEEE Trans. Biomed. Circuits Syst., vol. 5, no. 6, pp. 511-522, Dec. 2011.
[61] J. G. Webster, Medical Instrumentation: Application and Design, 4th Ed. NJ: Wiley, 2008.
[62] C. C. Enz and G. C. Temes, “Circuit techniques for reducing the effects of op-amp imperfections: Autozeroing, correlated double sampling, and chopper stabilization,” Proc. IEEE, vol. 84, no. 11, pp. 1584-1614, Nov. 1996.
[63] S. Y. Lee, J. H. Hong, C. H. Hsieh, M. C. Liang, S. Y. C. Chien, and K. H. Lin, “Low-power wireless ECG acquisition and classification system for body sensor networks,” IEEE J. Biomed. Health Inform., vol. 19, no. 1, pp. 236-246, Jan. 2015.
[64] J. Koh, Y. Choi, and G. Gomez, “A 66 dB DR 1.2 V 1.2 mW single amplifier double-sampling 2nd-order ADC for WCDMA in 90 nm CMOS,” IEEE Int. Solid-State Circuits Conf. Dig. Tech. Papers, Feb. 2005, vol. 1, pp. 170–171.
[65] K. Chandrashekar and B. Bakkaloglu, “A 10 b 50 MS/s opamp-sharing pipeline A/D with current-reuse OTAs,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 19, no. 9, pp. 1610-1616, Sept. 2011.
[66] D. Kanemoto, T. Ido, and K. Taniguchi, “A novel third order delta sigma modulator with one opamp shared among three integrator stages,” IEICE Electron. Express, vol. 5, no. 24, pp. 1088-1092, 2008.
[67] A. Pena-Perez, E. Bonizzoni, and F. Maloberti, “A 88-dB DR 84-dB SNDR very low-power single op-amp third-order Sigma-Delta modulator,” IEEE J. Solid-State Circuits, vol. 47, no. 9, pp. 2107-2118, Sep. 2012.
[68] R. G. Chang, C. Y. Chen, J. H. Hong, and S. Y. Lee, “Wide dynamic-range sigma-delta modulator with adaptive feed-forward coefficients,” IET Circuits, Devices Syst., vol. 4, no. 2, pp. 99-112, March 2010.
[69] R. del Río, F. Medeiro, B. Pérez-Verdú, J. M. de la Rosa, and Á. Rodríguez-Vázquez, CMOS Cascade Sigma-Delta Modulators for Sensors and Telecom: Error Analysis and Practical Design. Dordrecht: Springer, 2006.
[70] B. Wicht, T. Nirschl, and D. Schmitt-Landsiedel, “Yield and speed optimization of a latch-type voltage sense amplifier,” IEEE J. Solid-State Circuits, vol. 39, no. 7, pp. 1148-1158, July, 2004.
[71] P. Malcovati, S. Brigati, F. Francesconi, F. Maloberti, P. Cusinato, and A. Baschirotto, “Behavioral modeling of switched-capacitor sigma-delta modulators,” IEEE Trans. Circuits Syst. I. Fundam. Theory Appl., vol. 50, no. 3, pp. 352-364, March 2003.
[72] R. Muller, H.-P. Le, W. Li, P. Ledochowitsch, S. Gambini, T. Bjorninen, A. Koralek, J. M. Carmena, M. M. Maharbiz, E. Alon, and J. M. Rabaey, “A minimally invasive 64-channel wireless μECoG implant,” IEEE J. Solid-State Circuit, vol. 50, no. 1, pp.344-359, Jan. 2015.
[73] R. Mohan, S. Zaliasl, G. G. E. Gielen, C. V. Hoof, R. F. Yazicioglu, and N. V. Helleputte, “A 0.6-V, 0.015-mm2, time-based ECG readout for ambulatory applications in 40-nm CMOS,” IEEE J. Solid-State Circuits, vol. 52, no. 1, pp. 298–308, Jan. 2017.
[74] S. Rout and W. Serdijn, “High-pass ∆∑ converter design using a state-space approach and its application to cardiac signal acquisition,” IEEE Trans. Biomed. Circuits Syst., vol. 12, no. 3, pp.483-494, Jun. 2018.