Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e ⁺P achieve values of 98.13% and 96.91, respectively.

The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

The high mortality rate associated with cardiac abnormalities highlights the need of accurately detecting heart disorders in the early stage so to avoid severe health consequence for patients. Health trackers have become popular in the form of wearable devices. They are aimed to perform cardiac monitoring outside of medical clinics during peoples’ daily lives. Our paper proposes a new diagnostic algorithm and its implementation adopting a FPGA-based design. The conceived system automatically detects the most common arrhythmias and is also able to evaluate QT-segment lengthening and pulmonary embolism risk often caused by myocarditis. Debug and simulations have been carried out firstly in Matlab environment and then in Quartus IDE by Intel. The hardware implementation of the embedded system and the test for the functional accuracy verification have been performed adopting the DE1_SoC development board by Terasic, which is equipped with the Cyclone V 5CSEMA5F31C6 FPGA by Intel. Properly modified real ECG signals corrupted by a mixture of muscle noise, electrode movement artifacts, and baseline wander are used as a test bench. A value of 99.20% accuracy is achieved by taking into account 0.02 mV for the root mean square value of noise voltage. The implemented low-power circuit is suitable as a wearable decision support device.

Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is clinically worthwhile. VLPs are low-amplitude and high-frequency signals appearing at the end part of QRS complexes in the electrocardiogram, which can be considered as a robust feature for arrhythmia risk stratification in patients with cardiac diseases. This paper proposes a beat-to-beat VLP detection method based on the the marginal component analysis and investigates its performance taking into account different ratios between QRS and VLP power. After a denoising phase, performed adopting the singular vector decomposition technique, heartbeats characterized by VLP onsets are identified and extracted taking into account the vector magnitude of each high resolution ECG (HR-ECG) record. To evaluate the proposed method performance, a 15-lead HR-ECG database consisting of real VLP-negative and simulated VLP-positive patterns was used. The achieved results highlight the method validity for VLP detection.

The establishment of automatic diagnostic systems able to detect and classify skin lesions at the initial stage are getting really relevant and effective in providing support for medical personnel during clinical assessment. Image segmentation has a determinant part in computer-aided skin lesion diagnosis pipeline because it makes possible to extract and highlight information on lesion contour texture as, for example, skewness and area unevenness. However, artifacts, low contrast, indistinct boundaries, and different shapes and areas contribute to make skin lesion segmentation a challenging task. In this paper, a fully automatic computer-aided system for skin lesion segmentation in dermoscopic images is indicated. Adopting this method, noise and artifacts are initially reduced by the singular value decomposition; afterward lesion decomposition into a frame of bit-plane layers is performed. A specific procedure is implemented for redundant data reduction using simple Boolean operators. Since lesion and background are rarely homogeneous regions, the obtained segmentation region could contain some disjointed areas classified as lesion. To obtain a single zone classified as lesion avoiding spurious pixels or holes inside the image under test, mathematical morphological techniques are implemented. The performance obtained highlights the method validity.