A digital filter chip for ECG signal processing

New wireless technologies make possible the implementation of high level integration wireless devices which allow the replacement of traditional large wired monitoring devices. This kind of devices favours at-home hospitalization, reducing the affluence to sanitary assistance centers to make routine controls. This fact causes a really favourable social impact, especially for elder people, rural-zone inhabitant, chronic patients and handicapped people. Furthermore, it offers new functionalities to physicians and will reduce the sanitary cost. Among these functionalities, biomedical signals can be sent to other devices (screen, PDA, PC...) or processing centers, without restricting the patients' mobility. The aim of this project is the development and implementation of a reduced size multi-channel electrocardiograph based on IEEE 802.11, which allows wireless monitoring of patients, and the insertion of the information into the TCP/IP Hospital network.

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“…• Baseline drift and motion artifacts [9], [5]. Baseline drifts are produced by the movements of the thorax during breathing.…”

Section: B Digital Signal Processingmentioning

confidence: 99%

IEEE 802.11 ECG monitoring system

Tejero-Calado

López-Casado²,

Bernal-Martin³

et al. 2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

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“…One approach eliminates noise at the analog front-end [12]. It is well known that such analog filtering suffers from nonlinear phase response and a difficulty in making very deep attenuations (notches) at precise frequencies [13]. Furthermore, analog filters can have high sensitivity to component aging and variations over time—both unavoidable issues in long-term heart-monitoring with wearable ECG devices.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the authors in [13,15] have employed recursive running sum finite impulse response (FIR) digital filters [16,17] for the DC and 50-Hz notch filters with sampling rates of 200 Hz, as shown in Fig 2(A) (Throughout the paper, we shall call this Scenario I). Furthermore, the authors in [10] and [18] have employed the frequency response masking method [19] to notch DC, 60 Hz, 120 Hz and above for a 300-Hz sampling rate scenario, as shown in Fig 2(B) (We shall refer to this as Scenario II).…”

Section: Introductionmentioning

confidence: 99%

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A novel low-complexity digital filter design for wearable ECG devices

Asgari

Mehrnia

2017

PLoS ONE

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Wearable and implantable Electrocardiograph (ECG) devices are becoming prevailing tools for continuous real-time personal health monitoring. The ECG signal can be contaminated by various types of noise and artifacts (e.g., powerline interference, baseline wandering) that must be removed or suppressed for accurate ECG signal processing. Limited device size, power consumption and cost are critical issues that need to be carefully considered when designing any portable health monitoring device, including a battery-powered ECG device. This work presents a novel low-complexity noise suppression reconfigurable finite impulse response (FIR) filter structure for wearable ECG and heart monitoring devices. The design relies on a recently introduced optimally-factored FIR filter method. The new filter structure and several of its useful features are presented in detail. We also studied the hardware complexity of the proposed structure and compared it with the state-of-the-art. The results showed that the new ECG filter has a lower hardware complexity relative to the state-of-the-art ECG filters.

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“…The most widely used methods include, linear filtering [2], adaptive filtering, digital filtering, mathematical morphology filtering [3], Neural Networks, independent component analysis, empirical mode decomposition [4], fuzzy logic [5], fixed coefficients filtering [6] and wavelet transform based techniques [7,8].…”

Section: Introductionmentioning

confidence: 99%