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An Event-Based System for Low-Power ECG QRS Complex Detection
... Then, it marks the QRS complexes and extracts EB-heart-beats. This can be done, as previously shown in [31] , by re-implementing the gQRS-detection algorithm [32] to work with EB-sampled signals. Using the QRS timing information, the processing pipeline computes the instantaneous RR interval and uses it to define the heartbeats boundaries. ...
... 5. The EB-gQRS [31] algorithm finds the QRS complexes in the event-based signal and, consequently, subdivide the EB signal in EB heartbeats. 6. ...
Background and objective:
Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with standard interpolation techniques. In this work, we leverage the self-similarity of the electrocardiogram (ECG) signal to recover missing features in event-based sampled ECG signals, dynamically selecting patient-representative templates together with a novel dynamic time warping algorithm to infer the morphology of event-based sampled heartbeats.
Methods:
We acquire a set of uniformly sampled heartbeats and use a graph-based clustering algorithm to define representative templates for the patient. Then, for each event-based sampled heartbeat, we select the morphologically nearest template, and we then reconstruct the heartbeat with piece-wise linear deformations of the selected template, according to a novel dynamic time warping algorithm that matches events to template segments.
Results:
Synthetic tests on a standard normal sinus rhythm dataset, composed of approximately 1.8 million normal heartbeats, show a big leap in performance with respect to standard resampling techniques. In particular (when compared to classic linear resampling), we show an improvement in P-wave detection of up to 10 times, an improvement in T-wave detection of up to three times, and a 30% improvement in the dynamic time warping morphological distance.
Conclusion:
In this work, we have developed an event-based processing pipeline that leverages signal self-similarity to reconstruct event-based sampled ECG signals. Synthetic tests show clear advantages over classical resampling techniques.
... Then, it marks the QRS complexes and extracts EB-heartbeats. This can be done, as previously shown in [31], by reimplementing the gQRS-detection algorithm [32] to work with EB-sampled signals. Using the QRS timing information, the processing pipeline computes the instantaneous RR interval and uses it to define the heartbeats boundaries. ...
... Algorithm 1 Templates set update algorithm5. The EB-gQRS[31] algorithm finds the QRS complexes in the event-based signal and, consequently, subdivide the EB signal in EB heartbeats. 6. ...
Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with standard interpolation techniques. In this work, we leverage the self-similarity of the electrocardiogram (ECG) signal to recover missing features in event-based sampled ECG signals, dynamically selecting patient-representative templates together with a novel dynamic time warping algorithm to infer the morphology of event-based sampled heartbeats. Methods: We acquire a set of uniformly sampled heartbeats and use a graph-based clustering algorithm to define representative templates for the patient. Then, for each event-based sampled heartbeat, we select the morphologically nearest template, and we then reconstruct the heartbeat with piece-wise linear deformations of the selected template, according to a novel dynamic time warping algorithm that matches events to template segments. Results: Synthetic tests on a standard normal sinus rhythm dataset, composed of approximately 1.8 million normal heartbeats, show a big leap in performance with respect to standard resampling techniques. In particular (when compared to classic linear resampling), we show an improvement in P-wave detection of up to 10 times, an improvement in T-wave detection of up to three times, and a 30\% improvement in the dynamic time warping morphological distance. Conclusion: In this work, we have developed an event-based processing pipeline that leverages signal self-similarity to reconstruct event-based sampled ECG signals. Synthetic tests show clear advantages over classical resampling techniques.
... In the wearable domain, we are targeting energy-hungry applications aimed at continuous monitoring of biomedical signals. These applications usually work in a windowed fashion over long streams of data, and memory is the primary responsible for energy expenditure [18]. The objective of this experimental validation is to characterize the trade-off between the amount of input data that can be removed using our event-based approach, and the impact this has on the performance of the target task. ...
Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art systems are still built upon the classical Nyquist-based periodic signal acquisition. In this work, we design and validate the Polygonal Approximation Sampler (PAS), a novel circuit to implement a general-purpose event-based sampler using a polygonal approximation algorithm as the underlying sampling trigger. The circuit can be dynamically reconfigured to produce either a coarse or detailed reconstruction of the analog input by adjusting the error threshold of the approximation. The proposed circuit is designed at the Register Transfer Level and processes each input sample received from the analog-to-digital converter (ADC) in a single clock cycle. The PAS has been tested with three different types of archetypal signals captured by wearable devices (electrocardiogram, accelerometer, and respiration data) and compared with a standard periodic ADC. These tests show that single-channel signals, with slow variations and constant segments (like the used single-lead ECG and the respiration signals), take great advantage of the used sampling technique, reducing the amount of data used up to 99% without significant performance degradation. At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor of up to 80% with minor performance degradation. These results open the door to new types of wearable sensors with reduced size and higher battery lifetime.
... In the wearable domain, we are targeting applications aimed at continuous monitoring of biomedical signals, which are the most energy-hungry. These applications usually work in a windowed fashion over long streams of data, and memory is the primary responsible for energy expenditure [10]. The objective in this experimental validation is to characterize the trade-off between the amount of input data that can be removed using our event-based approach, and the impact this has in the performance of the target task. ...
Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing and storage. However, almost all state-of-the-art systems are still built upon the classical Nyquist-based periodic signal acquisition. In this work, we design and validate the Polygonal Approximation Sampler (PAS), a novel circuit to implement a general-purpose event-based sampler using a polygonal approximation algorithm as the underlying sampling trigger. The circuit can be dynamically reconfigured to produce a coarse or a detailed reconstruction of the analog input, by adjusting the error threshold of the approximation. The proposed circuit is designed at the Register Transfer Level and processes each input sample received from the ADC in a single clock cycle. The PAS has been tested with three different types of archetypal signals captured by wearable devices (electrocardiogram, accelerometer and respiration data) and compared with a standard periodic ADC. These tests show that single-channel signals, with slow variations and constant segments (like the used single-lead ECG and the respiration signals) take great advantage from the used sampling technique, reducing the amount of data used up to 99% without significant performance degradation. At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor up to 80% with minor performance degradation. These results open the door to new types of wearable sensors with reduced size and higher battery lifetime.
... While the design of highly efficient microcontrollers and transmission devices has been the main focus of research from industry and academia in the last years, aiming for energy efficient modern wearable systems, sensor data acquisition performed by medical devices is still mostly based on the standard paradigm of regular signal sampling at the Nyquist rate. Some innovative proposals are emerging, for example to create event-based heart-rate analysis devices, such as in the work by Zanoli et al. [102], where the proposed approach is compared to the standard one, on the same ULP platform, providing a reduction of the energy consumption in runtime up to 15.6 times, while keeping almost the same detection performance. The traditional acquisition approach based on sampling at the Nyquist rate leads to data overload and an extra use of resources in the full processing pipeline, when applied to sparse and highly non-stationary signals, like those typically handled by medical devices. ...
Wireless ElectroCardioGram (ECG) systems are employed in manifold application fields: tele-monitoring, sport applications, support to ageing people at home, fetal ECG, wearable devices and ambulatory monitoring. The presence of cables often hinders user’s free movements, alongside clinicians’ routine operations. Therefore, wireless ECG systems are desirable. This paper aims at reviewing the solutions described in the literature, besides commercially available devices and electronic components useful to setup laboratory prototypes. Several systems have been developed, different in terms of the adopted technology; when approaching the development of a wireless ECG system, some important aspects should be considered: electrodes (disposable, wet/dry, without contact, insulated), analog front-end, data acquisition systems (including amplifiers, multiplexer), wireless transmission technology (e.g. WiFi, Bluetooth) and power consumption (battery lifetime, miniaturization purposes). Technological advancements and continuous research have already brought to miniaturized and comfortable devices, but there is still room for improvement on multiple sides.
This paper presents a resource-efficient adaptive sampling methodology for classifying electrocardiogram (ECG) signals into different heart rhythms. We present our methodology in two folds: ( i ) the design of a novel real-time adaptive neural network architecture capable of classifying ECG signals with different sampling rates, and ( ii ) a runtime implementation of sampling rate control using deep reinforcement learning (DRL). By using essential morphological details contained in the heartbeat waveform, the DRL agent can control the sampling rate and effectively reduce energy consumption at runtime. To evaluate our adaptive classifier, we use the MIT-BIH database and the recommendation of the AAMI to train the classifiers. The classifier is designed to recognize three major types of arrhythmias which are supraventricular ectopic beats (SVEB), ventricular ectopic beats (VEB) and normal beats (N). The performance of the arrhythmia classification reaches an accuracy of 97.2% for SVEB, and 97.6% for VEB beats. Moreover, the designed system is 7.3 × more energy efficient compared to the baseline architecture, where the adaptive sampling rate is not utilized. The proposed methodology can provide reliable and accurate real time ECG signal analysis with performances comparable to state-of-the-art methods. Given its time-efficient, low-complexity, and low-memory-usage characteristics, the proposed methodology is also suitable for practical ECG applications, in our case for arrhythmia classification, using resource-constrained devices, especially wearable healthcare devices and implanted medical devices.
We report an always-on event-driven asynchronous wake-up circuit with trainable pattern recognition capabilities to duty-cycle power-constrained Internet-of-Things (IoT) sensor nodes. The wake-up circuit is based on a level-crossing analogto-digital converter (LC-ADC) employed as a feature-extraction block with automatic activity-sampling rate scaling behavior. A novel asynchronous digital logic classifier for sequential pattern recognition is presented. It is driven by the LC-ADC activity and trained to minimize classification errors due to falsely detected events. As proof-of-concept, a prototype of the wake-up circuit is fabricated in 130nm CMOS technology within 0.054 mm
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of active area, covering up to 2.6 kHz of input signal bandwidth. The prototype has been first validated by interfacing it with a commercial accelerometer to classify hand gestures in real-time, reaching 81% of accuracy with only 2.2 μW at 1-V supply. To highlight the flexibility of the design, a second application, detecting pathologic electrocardiogram beats is also discussed.
Wearable wireless biomedical sensors will play important roles in future prevention-oriented healthcare. The strictly restricted power budget for the wearables rendered Nyquist sampling scheme unsuitable for this type of sensors. This is because the Nyquist sampling does not make use of the sparse characteristics of biomedical signal. In this paper we presented an alternative sampling scheme, called event-driven sampling, which takes advantage of sparsity in the biomedical signal and generates considerable fewer sampling points compared to Nyquist sampling scheme. Traditionally, event-driven ADC uses two comparators to generate outputs, which limits the minimum power of the ADC. We propose a new ADC structure that utilizes only one high-resolution comparator to generate output while a low-resolution comparator determines the direction of input. In this way, the minimum power of a level-crossing ADC can be reduced almost half compared to traditional level-crossing ADC.
The long-standing analog-to-digital conversion paradigm based on Shannon/Nyquist sampling has been challenged lately, mostly in situations such as radar and communication signal processing where signal bandwidth is so large that sampling architectures constraints are simply not manageable. Compressed sensing (CS) is a new emerging signal acquisition/compression paradigm that offers a striking alternative to traditional signal acquisition. Interestingly, by merging the sampling and compression steps, CS also removes a large part of the digital architecture and might thus considerably simplify analog-to-information (A2I) conversion devices. This so-called “analog CS,” where compression occurs directly in the analog sensor readout electronics prior to analog-to-digital conversion, could thus be of great importance for applications where bandwidth is moderate, but computationally complex, and power resources are severely constrained. In our previous work (Mamaghanian, 2011), we quantified and validated the potential of digital CS systems for real-time and energy-efficient electrocardiogram compression on resource-constrained sensing platforms. In this paper, we review the state-of-the-art implementations of CS-based signal acquisition systems and perform a complete system-level analysis for each implementation to highlight their strengths and weaknesses regarding implementation complexity, performance and power consumption. Then, we introduce the spread spectrum random modulator pre-integrator (SRMPI), which is a new design and implementation of a CS-based A2I read-out system that uses spread spectrum techniques prior to random modulation in order to produce the low rate set of digital samples. Finally, we experimentally built an SRMPI prototype to compare it with state-of-the-art CS-based signal acquisition systems, focusing on critical system design parameters and constraints, and show that this new proposed architecture offers a compelling alternativ- , in particular for low power and computationally-constrained embedded systems.
This paper presents a new sampling technique and a successive approximation analog to digital converter (SA-ADC) which samples sparse signals in a non-uniform adaptive way. The proposed sampling technique has the capability to be incorporated in the structure of the SA-ADC. The proposed SA-ADC changes the rate of sampling in accordance with the rate of changes of the signal. In this way, the data volume is reduced considerably without losing the important information in the signal. Simulation results in the 0.18 um CMOS technology shows a power saving of up to 90.5 % and a compression ratio of 7.5 compared to the conventional sampling technique of ECG signals.
The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.
The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.
QRS detectors performance comparison in public databases
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Event-Based Control and Signal Processing
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Chapter 7 - ECG Signal Processing