Wireless Body Sensor Nodes (WBSNs) are miniaturized, wearable systems able to measure and wirelessly transmit biological signals of patients. A major field of application of WBSNs is the ambulatory acquisition of electrocardiograms (ECGs), which evaluates the electrical activity of the heart. In this context, these devices allow long time monitoring of subjects producing little discomfort and requiring minimal medical supervision.
A recent trend in WBSNs, driven by the progress in semiconductor technologies, has been the emergence of “smart” wireless nodes [1]. These smart WBSNs (FIG. 1) can, in addition to acquiring and transmitting data wirelessly, perform advanced digital signal processing filtering noise typically corrupting the signals and/or executing an automated diagnosis.
In the field of ambulatory electrocardiography, an important early diagnosis step is to separate normal and pathological heartbeats. The benefit of this separation is two-fold: first, it can provide helpful information for speeding up the visual inspections of lengthy recordings by medical staff; second, it can be used to activate a more detailed analysis of only those beats presenting pathological characteristics.
The second scenario can lead to two non-obvious, yet substantial, benefits. On the one hand, if a detailed diagnosis is performed off-node, it can be desirable to transmit or store only pathological beats on the WBSN, greatly reducing either the energy employed for wireless transmission or the data storage requirements, respectively. On the other hand, even if the analysis of beats is executed on the wireless node, computation effort can be reduced by activating it only when abnormal beats are detected, increasing energy efficiency.
While off-line algorithms have been proposed to classify the different heartbeat morphologies [2][3], their real-time implementation poses a considerable challenge on WBSN platforms, due to their high computational requirements.
A neuro-fuzzy classifier (NFC) approach [4] is instead potentially a good candidate for this application. Its simple feed-forward structure makes it eligible to be optimized for, and executed on, the constrained resources typically present on WBSNs.
An important factor to consider is the high dimensionality of the heartbeat representation problem as, for every beat, tens of samples before and after the R peak of the QRS complex peak have to be considered to perform a reliable classification.
To effectively address this problem, in the method according to the invention enables to classify ECG beats using a method based on random projections (RPs) that can lead to reduction in input size. The approximation error introduced by random projections is theoretically bounded, nonetheless empirical evidence shows that certain projections perform better than others. Our experiments show that even a rather simple optimization, such as the one performed by a genetic algorithm in few generations, can find a proper projection to obtain optimal classification results. In one embodiment of the invention, the method includes the RP-classifier, a filtering stage and a peak detector, used to isolate beats. In one embodiment, three-lead delineation is used instead adopted as an example of detailed analysis, and is activated by the classifier only for beats identified as pathological.
Test heartbeats were retrieved from the MIT-BIH Arrhythmia database [5], considering all beats presenting three different morphologies: normal sinus rhythms, left bundle branch blocks and premature ventricular contractions. Experimental results show that the proposed methodology can identify more than 97% of abnormal beats, while using a small fraction of the available SoC memory and computing resources.
Neuro-fuzzy classifiers (NFCs) [6] have been extensively studied in the literature. Their ability to explicitly express uncertainty in classification, given by the employed fuzzy values, makes them particularly well suited to the problem of heartbeat classification, as proposed in [7].
NFCs can be effectively trained using established methods, the most common being the gradient descent algorithm described in [6] and the scale conjugate gradient introduced in [8] and [9], which is employed in this work. The approach is both computationally simpler and presenting lower memory requirements than comparable methods, like the ones based on support vector machines [2] and linear discriminants [10]. Therefore, it is more suitable for execution in embedded WBSNs.
Several state-of-the-art strategies for neuro-fuzzy classification of ECGs can be distinguished based on the methodology employed to extract the features of individual heartbeats generating the classifier input. Solutions to the features extraction problem include the ones based on Independent or Principal component analysis (ICA [11]/PCA, [3]), Discrete Wavelets Transform (DWT [12]) and Discrete Cosine Transform (DCT [4]). As opposed to the method disclosed in this invention, these methods demand a computation effort not compatible with WBSN resources.
A different approach, based on detection of morphological features, is presented in [13]; it nevertheless requires a detailed analysis of heartbeats before classification, conversely to our goal of early identification of pathological beats.