12 research outputs found
automatic detection of complete and measurable cardiac cycles in antenatal pulsed wave doppler signals
Abstract Background and objective Pulsed-wave Doppler (PWD) echocardiography is the primary tool for antenatal cardiological diagnosis. Based on it, different measurements and validated reference parameters can be extracted. The automatic detection of complete and measurable cardiac cycles would represent a useful tool for the quality assessment of the PWD trace and the automated analysis of long traces. Methods This work proposes and compares three different algorithms for this purpose, based on the preliminary extraction of the PWD velocity spectrum envelopes: template matching, supervised classification over a reduced set of relevant waveshape features, and supervised classification over the whole waveshape potentially representing a cardiac cycle. A custom dataset comprising 43 fetal cardiac PWD traces (174,319 signal segments) acquired on an apical five-chamber window was developed and used for the assessment of the different algorithms. Results The adoption of a supervised classifier trained with the samples representing the upper and lower envelopes of the PWD, with additional features extracted from the image, achieved significantly better results (p Conclusions The results reveal excellent detection performance, suggesting that the proposed approach can be adopted for the automatic analysis of long PWD traces or embedded in ultrasound machines as a first step for the extraction of measurements and reference clinical parameters
Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography
Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated.Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels.Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden
Adaptive Filtering for Electromyographic Signal Processing in Scoliosis Indexes Estimation
Adolescent idiopathic scoliosis is defined as a three-dimensional deformity of the spine and trunk occurring in about 2.5% of most populations. It is usually analyzed radiographically, but electromyography (EMG) can be also used, since muscles activity is correlated to deformity progression. EMG ratio is a numerical index used in the literature to provide information about scoliosis progression. Trunk EMG recordings are strongly affected by the electrocardiogram (ECG) of the subject. Previous studies removed this interference from the EMG signal by blanking the QRS complexes of the ECG but, as a consequence, several segments of the signal are removed. Furthermore, the other relevant ECG waves such as P and T are not cancelled and can invalidate the computation of parameters such as the EMG ratio. The aim of this study is to evaluate the possibility, by means of a modified recording protocol including further electrodes, to completely remove the ECG interference by adopting a multi-ref erence recursive least square (RLS) adaptive filter. The results of the study reveal how the complete clearing of the ECG from the EMG channels leads to different numerical values of the index, compared to the QRS blanking, more reliable and meaningful for the clinicians
Annotated real and synthetic datasets for non-invasive foetal electrocardiography post-processing benchmarking
Comparative evaluation of different wavelet thresholding methods for neural signal processing
reserved6Neural signal decoding is the basis for the development of neuroprosthetic devices and systems. Depending on the part of the nervous system these signals are picked up from, different signal-to-noise ratios (SNR) can be experienced. Wavelet denoising is often adopted due to its capability of reducing, to some extent, the noise falling within the signal spectrum. Several variables influence the denoising quality, but usually the focus in on the selection of the best performing mother wavelet. However, the threshold definition and the way it is applied to the signal have a significant impact on the denoising quality, determining the amount of noise removed and the distortion introduced on the signal. This work presents a comparative analysis of different threshold definition and thresholding mechanisms on neural signals, either largely adopted for neural signal processing or not. In order to evaluate the quality of the denoising in terms of the introduced distortion, which is important when decoding is implemented through spike-sorting algorithms, a synthetic dataset built on real action potentials was used, creating signals with different SNR and characterized by an additive white Gaussian noise (AWGN). The obtained results reveal the superiority of an approach, originally conceived for noisy non-linear time series, over the more typical ones. When compared to the original signal, a correlation above 0.9 was obtained, while in terms of root mean square error (RMSE) an improvement of 13% and 33% was reported with respect to the Minimax and Universal thresholds respectively.mixedG. Barabino; G. Baldazzi; E. Sulas; C. Carboni; L. Raffo; D. PaniBarabino, G.; Baldazzi, G.; Sulas, E.; Carboni, C.; Raffo, L.; Pani, D
Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography
Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography
Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated.Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels.Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden
K Index is a Reliable Marker of Intrathecal Synthesis, and an Alternative to IgG Index in Multiple Sclerosis Diagnostic Work-Up
The K free light chain (K) index has been suggested as a reliable marker of intrathecal synthesis, despite the 2017 McDonald criteria for multiple sclerosis (MS) suggesting to “interpret with caution positive immunoglobulin G (IgG) index when testing for oligoclonal bands (OB) is negative or not performed„. The aim of this study was to compare the performance of K and IgG indexes for MS diagnosis and OB detection in a cohort of Italian patients. We enrolled 385 patients (127 MS, 258 non-MS) who had cerebrospinal fluid (CSF) analysis, including isoelectric focusing (IEF), to detect OB in the diagnostic work-up. Albumin, IgG and free light chains were measured by nephelometry and used to calculate IgG and K indexes. Although the two markers were highly related (r = 0.75, r2 = 0.55, p < 0.0001), the K index showed greater sensitivity and negative predictive value (versus the IgG index) for OB detection (97% versus 48% and 97% versus 71%) and MS diagnosis (96% versus 50% and 98% versus 78%). These results support K index (and not IgG index) as a first-line marker for MS, followed by IEF, according to a sequential testing approach in CSF analysis
Accuracy and feasibility of piezoelectric inkjet coating technology for applications in microneedle-based transdermal delivery
Coated microneedles have shown immense promise for use in transdermal delivery and diagnostics, due to their ability to painlessly breach the skin's outermost stratum corneum layer and interact with the epidermal layers immediately beneath. In this work, we use an off-the-shelf piezoelectric dispensing system to demonstrate the feasibility of depositing material directly on to steeply-sloping microneedle sidewalls, without the need for specific needle array positioning or material pretreatment. In the first instance, an analysis of deposition accuracy shows that over 95% of dispensed droplets land within 20 μm of the target. Through the use of sequential dispense and drying steps, 3.2 nL of a model drug formulation has been deposited onto both silicon and polymeric microneedles with highly sloped (71°) sidewalls; these are the steepest surfaces that have been coated to date. Finally, preliminary ex-vivo skin studies have been performed to show that the material may be successfully transferred from the needle to skin. Despite the smooth surfaces, ultrasharp tips and steep sidewalls of these structures, piezoelectric dispense techniques are clearly feasible for microneedle coating and may offer a promising alternative to conventional coating processes