A low power fuzzy logic based variable resolution ADC for wireless ECG monitoring systems

Abstract Portable low power and high quality monitoring devices play an important role for improving the performance of wireless body sensor networks. Mixed signal processor is required in ECG monitoring unit that comprises of analog front end, analog to digital converter (ADC) and digital signal processor. ADC is a core element in mixed signal processing unit and the proper design of ADC is crucial for data acquisition without losing heart related information and originality. The adaptive sampling has been used in the existing ADCs for choosing the sampling clock adaptively. Adaptive sampling rate ADC performs compression and reduces power consumption to some extent, however low powers ADCs are essential in wireless healthcare monitoring systems. In this paper, fuzzy logic based variable resolution controller is proposed to design the ADC efficiently in terms of circuit complexity and power. To further reduce the power consumption, power gating technique is adopted for static power reduction. Under 90 nm CMOS technology, gate count, core area utilization and power consumption have been determined for existing adaptive sampling ADC and fuzzy logic based adaptive sampling ADC. Cadence design tools have been used for the measurement of power in the designed circuit. The performance has been found to be better in terms of area and power that are very much essential for wireless ECG monitoring systems.

[1]  T. Devolder,et al.  Self-Enabled “Error-Free” Switching Circuit for Spin Transfer Torque MRAM and Logic , 2012, IEEE Transactions on Magnetics.

[2]  Refet Firat Yazicioglu,et al.  ECG Signal Compression and Classification Algorithm With Quad Level Vector for ECG Holter System , 2010, IEEE Transactions on Information Technology in Biomedicine.

[3]  Yusuke Shuto,et al.  Nonvolatile Power-Gating Field-Programmable Gate Array Using Nonvolatile Static Random Access Memory and Nonvolatile Flip-Flops Based on Pseudo-Spin-Transistor Architecture with Spin-Transfer-Torque Magnetic Tunnel Junctions , 2012 .

[4]  Álvaro Alesanco Iglesias,et al.  An Integrated Healthcare Information System for End-to-End Standardized Exchange and Homogeneous Management of Digital ECG Formats , 2012, IEEE Transactions on Information Technology in Biomedicine.

[5]  Matt Welsh,et al.  Sensor networks for emergency response: challenges and opportunities , 2004, IEEE Pervasive Computing.

[6]  Marcela D. Rodríguez,et al.  Location-aware access to hospital information and services , 2004, IEEE Transactions on Information Technology in Biomedicine.

[7]  Hannu Tenhunen,et al.  Bio-Patch Design and Implementation Based on a Low-Power System-on-Chip and Paper-Based Inkjet Printing Technology , 2012, IEEE Transactions on Information Technology in Biomedicine.

[8]  Qiang Fang,et al.  A wireless ECG acquisition SoC for body sensor network , 2012, 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[9]  Jan Larsen,et al.  An Electronic Patch for Wearable Health Monitoring by Reflectance Pulse Oximetry , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[10]  M. Sabrigiriraj,et al.  A New LMS Based Noise Removal and DWT Based R-peak Detection in ECG Signal for Biotelemetry Applications , 2014 .

[11]  M. Sabrigiriraj,et al.  VLSI implementation of a new LMS-based algorithm for noise removal in ECG signal , 2016 .

[12]  Seulki Lee,et al.  A 3.9 mW 25-Electrode Reconfigured Sensor for Wearable Cardiac Monitoring System , 2011, IEEE Journal of Solid-State Circuits.

[13]  P.E. Ross Managing care through the air [remote health monitoring] , 2004, IEEE Spectrum.

[14]  Min Chen,et al.  WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications , 2014, IEEE Journal of Biomedical and Health Informatics.

[15]  Yong Lian,et al.  A 0.7-V 17.4-/spl mu/W 3-Lead Wireless ECG SoC , 2013, IEEE Transactions on Biomedical Circuits and Systems.

[16]  Bozena Kaminska,et al.  Mechanically Flexible Wireless Multisensor Platform for Human Physical Activity and Vitals Monitoring , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[17]  Xing Hongyan,et al.  A New QRS Detection Algorithm Based on Empirical Mode Decomposition , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[18]  Pradip Bose,et al.  Microarchitectural techniques for power gating of execution units , 2004, Proceedings of the 2004 International Symposium on Low Power Electronics and Design (IEEE Cat. No.04TH8758).

[19]  M. Sabarimalai Manikandan,et al.  A novel method for detecting R-peaks in electrocardiogram (ECG) signal , 2012, Biomed. Signal Process. Control..