QRS Detector Performance Evaluation Aware of Temporal Accuracy and Presence of Noise.

Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the accuracy expressed by missed or redundant events statistics is often the only parameter used to evaluate the detector's performance. In this paper, we first notice that statistics of true positive detections rely on researchers' arbitrary selection of time tolerance between QRS detector output and the database reference. Next, we propose a multidimensional algorithm evaluation method and present its use on four example QRS detectors. The dimensions are (a) influence of detection temporal tolerance, tested for values between 8.33 and 164 ms; (b) noise immunity, tested with an ECG signal with an added muscular noise pattern and signal-to-noise ratio to the effect of "no added noise", 15, 7, 3 dB; and (c) influence of QRS morphology, tested on the six most frequently represented morphology types in the MIT-BIH Arrhythmia Database. The multidimensional evaluation, as proposed in this paper, allows an in-depth comparison of QRS detection algorithms removing the limitations of existing one-dimensional methods. The method enables the assessment of the QRS detection algorithms according to the medical device application area and corresponding requirements of temporal accuracy, immunity to noise, and QRS morphology types. The analysis shows also that, for some algorithms, adding muscular noise to the ECG signal improves algorithm accuracy results.

Measurement Systems for Use in the Navigation of the Cannula-Guide Assembly within the Deep Regions of the Bronchial Tree.

BACKGROUND: The purpose of this paper is to present the spatial navigation system prototype for localizing the distal tip of the cannula-guide assembly. This assembly is shifted through the channel of a bronchoscope, which is fixed in relation to the patient. The navigation is carried out in the bronchial tree, based on maneuvers of the aforementioned assembly. METHODS: The system consists of three devices mounted on the guide handle and at the entrance to the bronchoscope working channel. The devices record the following values: cannula displacement, rotation of the guide handle, and displacement of the handle ring associated with the bending of the distal tip of the guide. RESULTS: In laboratory experiments, we demonstrate that the cannula displacement can be monitored with an accuracy of 2 mm, and the angles of rotation and bending of the guide tip with an accuracy of 10 and 20 degrees, respectively, which outperforms the accuracy of currently used methods of bronchoscopy support. CONCLUSIONS: This accuracy is crucial to ensure that we collect the material for histopathological examination from a precisely defined place. It makes it possible to reach cancer cells at their very early stage.

Touchless Heart Rate Monitoring from an Unmanned Aerial Vehicle Using Videoplethysmography.

Motivation: The advancement of preventive medicine and, subsequently, telemedicine drives the need for noninvasive and remote measurements in patients' natural environments. Heart rate (HR) measurements are particularly promising and extensively researched due to their quick assessment and comprehensive representation of patients' conditions. However, in scenarios such as endurance training or emergencies, where HR measurement was not anticipated and direct access to victims is limited, no method enables obtaining HR results that are suitable even for triage. Methods: This paper presents the possibility of remotely measuring of human HR from a series of in-flight videos using videoplethysmography (VPG) along with skin detection, human pose estimation and image stabilization methods. An unmanned aerial vehicle (UAV) equipped with a camera captured ten segments of video footage featuring volunteers engaged in free walking and running activities in natural sunlight. The human pose was determined using the OpenPose algorithm, and subsequently, skin areas on the face and forearms were identified and tracked in consecutive frames. Ultimately, HR was estimated using several VPG methods: the green channel (G), green-red difference (GR), excess green (ExG), independent component analysis (ICA), and a plane orthogonal to the skin (POS). Results: When compared to simultaneous readings from a reference ECG-based wearable recorder, the root-mean-squared error ranged from 17.7 (G) to 27.7 (POS), with errors of less than 3.5 bpm achieved for the G and GR methods. Conclusions: These results demonstrate the acceptable accuracy of touchless human pulse measurement with the accompanying UAV-mounted camera. The method bridges the gap between HR-transmitting wearables and emergency HR recorders, and it has the potential to be advantageous in training or rescue scenarios in mountain, water, disaster, or battlefield settings.

Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks.

Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother's mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks.

Prediction of Preterm Delivery from Unbalanced EHG Database.

OBJECTIVE: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. METHOD: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager-Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. MAIN RESULTS: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. SIGNIFICANCE: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.

Diagnostic Interpretation of Non-Uniformly Sampled Electrocardiogram.

We present a set of three fundamental methods for electrocardiogram (ECG) diagnostic interpretation adapted to process non-uniformly sampled signal. The growing volume of ECGs recorded daily all over the world (roughly estimated to be 600 TB) and the expectance of long persistence of these data (on the order of 40 years) motivated us to challenge the feasibility of medical-grade diagnostics directly based on arbitrary non-uniform (i.e., storage-efficient) ECG representation. We used a refined time-independent QRS detection method based on a moving shape matching technique. We applied a graph data representation to quantify the similarity of asynchronously sampled heartbeats. Finally, we applied a correlation-based non-uniform to time-scale transform to get a multiresolution ECG representation on a regular dyadic grid and to find precise P, QRS and T wave delimitation points. The whole processing chain was implemented and tested with MIT-BIH Database (probably the most referenced cardiac database) and CSE Multilead Database (used for conformance testing of medical instruments) signals arbitrarily sampled accordingly to a perceptual model (set for variable sampling frequency of 100-500 Hz, compression ratio 3.1). The QRS detection shows an accuracy of 99.93% with false detection ratio of only 0.18%. The classification shows an accuracy of 99.27% for 14 most frequent MIT-BIH beat types and 99.37% according to AAMI beat labels. The wave delineation shows cumulative (i.e., sampling model and non-uniform processing) errors of: 9.7 ms for P wave duration, 3.4 ms for QRS, 6.7 ms for P-Q segment and 17.7 ms for Q-T segment, all the values being acceptable for medical-grade interpretive software.

Adaptive Sampling of the Electrocardiogram Based on Generalized Perceptual Features.

A non-uniform distribution of diagnostic information in the electrocardiogram (ECG) has been commonly accepted and is the background to several compression, denoising and watermarking methods. Gaze tracking is a widely recognized method for identification of an observer's preferences and interest areas. The statistics of experts' scanpaths were found to be a convenient quantitative estimate of medical information density for each particular component (i.e., wave) of the ECG record. In this paper we propose the application of generalized perceptual features to control the adaptive sampling of a digital ECG. Firstly, based on temporal distribution of the information density, local ECG bandwidth is estimated and projected to the actual positions of components in heartbeat representation. Next, the local sampling frequency is calculated pointwise and the ECG is adaptively low-pass filtered in all simultaneous channels. Finally, sample values are interpolated at new time positions forming a non-uniform time series. In evaluation of perceptual sampling, an inverse transform was used for the reconstruction of regularly sampled ECG with a percent root-mean-square difference (PRD) error of 3-5% (for compression ratios 3.0-4.7, respectively). Nevertheless, tests performed with the use of the CSE Database show good reproducibility of ECG diagnostic features, within the IEC 60601-2-25:2015 requirements, thanks to the occurrence of distortions in less relevant parts of the cardiac cycle.

Assisted Living System with Adaptive Sensor's Contribution.

Multimodal sensing and data processing have become a common approach in modern assisted living systems. This is widely justified by the complementary properties of sensors based on different sensing paradigms. However, all previous proposals assume data fusion to be made based on fixed criteria. We proved that particular sensors show different performance depending on the subject's activity and consequently present the concept of an adaptive sensor's contribution. In the proposed prototype architecture, the sensor information is first unified and then modulated to prefer the most reliable sensors. We also take into consideration the dynamics of the subject's behavior and propose two algorithms for the adaptation of sensors' contribution, and discuss their advantages and limitations based on case studies.

Optimizing Wavelet ECG Watermarking to Maintain Measurement Performance According to Industrial Standard.

Watermarking is currently investigated as an efficient and safe method of embedding additional patient or environment-related data into the electrocardiogram. This paper presents experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view. We implemented an original time-frequency watermarking technique with an adaptive beat-to-beat lead-independent data container design. We tested six wavelets, six coding bit depth values (including the automatic noise-dependent one) and two types of watermark content to find the conditions that are necessary for watermarked ECG to maintain the compliance with International Electrotechnical Commission (IEC) requirements for interpretation performance. Unlike other authors, we did not assess the differences of signal values, but errors in ECG wave delineation results. The results of a total of 7300 original and watermarked 10 s ECGs were statistically processed to reveal possible interpretation quality degradation due to watermarking. Finally we found (1) the Symlet of 11-th order as the best of the wavelets that were tested; (2) the important role of ECG wave delineation and noise tracking procedures; (3) the high influence of the watermark-to-noise similarity of amplitude and values distribution and (4) the stability of the watermarking capacity for different heart rates in atrial rhythms.

Seamless tracing of human behavior using complementary wearable and house-embedded sensors.

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system.

Sex Differentiation of Trabecular Bone Structure Based on Textural Analysis of Pelvic Radiographs.

Objectives: The purpose of this paper is to assess the determination of male and female sex from trabecular bone structures in the pelvic region. The study involved analyzing digital radiographs for 343 patients and identifying fourteen areas of interest based on their medical significance, with seven regions on each side of the body for symmetry. Methods: Textural parameters for each region were obtained using various methods, and a thorough investigation of data normalization was conducted. Feature selection approaches were then evaluated to determine a small set of the most representative features, which were input into several classification machine learning models. Results: The findings revealed a sex-dependent correlation in the bone structure observed in X-ray images, with the degree of dependency varying based on the anatomical location. Notably, the femoral neck and ischium regions exhibited distinctive characteristics between sexes. Conclusions: This insight is crucial for medical professionals seeking to estimate sex dependencies from such image data. For these four specific areas, the balanced accuracy exceeded 70%. The results demonstrated symmetry, confirming the genuine dependencies in the trabecular bone structures.

Fusion of EEG and Eye Blink Analysis for Detection of Driver Fatigue.

OBJECTIVE: The driver fatigue detection using multi-channel electroencephalography (EEG) has been extensively addressed in the literature. However, the employment of a single prefrontal EEG channel should be prioritized as it provides users with more comfort. Furthermore, eye blinks from such channel can be analyzed as the complementary information. Here, we present a new driver fatigue detection method based on simultaneous EEG and eye blinks analysis using an Fp1 EEG channel. METHODS: First, the moving standard deviation algorithm identifies eye blink intervals (EBIs) to extract blink-related features. Second, the discrete wavelet transform filters the EBIs from the EEG signal. Third, the filtered EEG signal is decomposed into sub-bands, and various linear and nonlinear features are extracted. Finally, the prominent features are selected by the neighbourhood components analysis and fed to a classifier to discriminate between fatigue and alert driving. In this paper, two different databases are investigated. The first one is used for parameters' tuning of proposed method for the eye blink detection and filtering, nonlinear EEG measures, and feature selection. The second one is solely used for testing the robustness of the tuned parameters. MAIN RESULTS: The comparison between the obtained results from both databases by the AdaBoost classifier in terms of sensitivity (90.2% vs. 87.4%), specificity (87.7% vs. 85.5%), and accuracy (88.4% vs. 86.8%) indicates the reliability of the proposed method for the driver fatigue detection. SIGNIFICANCE: Considering the existence of commercial single prefrontal channel EEG headbands, the proposed method can be used to detect the driver fatigue in real-world scenarios.

Simultaneous Eye Blink Characterization and Elimination From Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection.

OBJECTIVE: Blink-related features derived from electroencephalography (EEG) have recently arisen as a meaningful measure of driver's cognitive state. Combined with band power features of low-channel prefrontal EEG data, blink-derived features enhance the detection of driver drowsiness. Yet, it remains unanswered whether synergy of combined blink and EEG band power features for the detection of driver drowsiness may be further boosted if a proper eye blink removal is also applied before EEG analysis. This paper proposes an algorithm for simultaneous eye blink feature extraction and elimination from low-channel prefrontal EEG data. METHODS: Firstly, eye blink intervals (EBIs) are identified from the Fp1 EEG channel using variational mode extraction, and then blink-related features are derived. Secondly, the identified EBIs are projected to the rest of EEG channels and then filtered by a combination of principal component analysis and discrete wavelet transform. Thirdly, a support vector machine with 10-fold cross-validation is employed to classify alert and drowsy states from the derived blink and filtered EEG band power features. MAIN RESULTS: When compared the synergy of eye blink and EEG features before and after filtering by the proposed algorithm, a significant improvement in the mean accuracy of driver drowsiness detection was achieved (71.2% vs. 78.1%, p 0.05). SIGNIFICANCE: This paper validates a novel view of eye blinks as both a source of information and artifacts in EEG-based driver drowsiness detection.

Wearable wireless heart rate monitor for continuous long-term variability studies.

Prototyping of a home care system for activity surveillance and sleep assessment targeted to elderly people involves the compromise of wearing comfort and measurement performance. We propose a wearable heart rate variability monitor connected via wireless digital link to a home-embedded infrastructure of multimodal health surveillance system. The coin-size wearable recorder acquires and processes the electrocardiogram and sends annotated tachogram data accordingly to the subject's status and programed schedule. Thanks to remote programmability, in case of predefined thresholds excess, the recorder response is immediate, whereas the regular reports are organized in packets and delivered in bulk in short transmission sessions. This approach significantly reduces the data rate and the energy required to supply the communication module. The prototype weighting 11.2 g is based on the ARM7 (Atmel Corporate Headquarters 2325 Orchard Parkway San Jose, CA, USA) processor running at 18 MHz and with a 300-mA h rechargeable battery allows for up to 10 days of seamless tachogram monitoring.

VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel.

OBJECTIVE: Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel. METHOD: The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel. RESULTS: The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from -8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87). SIGNIFICANCE: The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference.

A Direct Transform of Discrete Non-Uniform ECG to a Time-Scale Representation.

Adaptive sampling is an interesting alternative for biosignal acquisition, transmission and storage, however further processing of non uniform representations is still waiting for development. In this paper a direct non-uniform to time-scale (NUTS) transform is presented and applied to the ECG signal. Well accepted limits of bandwidth in particular sections of the ECG and established standards for the assessment of diagnostic quality help in evaluation of the influence the transform has to the diagnostic result. The transform uses a regular-grid Coiflet 5-th order nearly symmetric wavelet, but the novelty is a pointwise calculating of its correlation accordingly to non-uniform distribution of the electrocardio-gram samples. In tests with CSE Database files the proposed transform method yields not bit-accurate ECG signals, but the diagnostic results are more influenced by the non-uniform representation (for QRS mean deviation: +0.7 ms vs. original files) than by the transform itself (for QRS additionally: +0.6 ms) and all the results remain within the accuracy tolerance of the CEN industrial standard.

Differential Watermarking of Multilead ECG Baseline.

Digital watermarking has been widely recognized as an effective tool for embedment of auxiliary data in the host record. This paper presents a new method of watermarking using lead-to-lead difference of values in the baseline of the host electrocardiogram. The method starts with delineation of the baseline and uses Kirchoff voltage law or interpolation to predict any selected lead from the remaining ones. Next, the difference between the predicted and actual value is considered as noise and subjects to measurement of level and distribution in the time frame of baseline. The watermark with patient data or results of accompanying measurements is coded accordingly to mimic the noise. Replacement of the baseline noise with the watermark data ends the process. With 12-lead CSE files and respective reference borders of PQ and TP segments, the capacity of watermark achieved 3875 bits per second, while the diagnostic value of the ECG remains untouched.

Graph-based representation of behavior in detection and prediction of daily living activities.

Various surveillance systems capture signs of human activities of daily living (ADLs) and store multimodal information as time line behavioral records. In this paper, we present a novel approach to the analysis of a behavioral record used in a surveillance system designed for use in elderly smart homes. The description of a subject's activity is first decomposed into elementary poses - easily detectable by dedicated intelligent sensors - and represented by the share coefficients. Then, the activity is represented in the form of an attributed graph, where nodes correspond to elementary poses. As share coefficients of poses are expressed as attributes assigned to graph nodes, their change corresponding to a subject's action is represented by flow in graph edges. The behavioral record is thus a time series of graphs, which tiny size facilitates storage and management of long-term monitoring results. At the system learning stage, the contribution of elementary poses is accumulated, discretized and probability-ordered leading to a finite list representing the possible transitions between states. Such a list is independently built for each room in the supervised residence, and employed for assessment of the current action in the context of subject's habits and a room purpose. The proposed format of a behavioral record, applied to an adaptive surveillance system, is particularly advantageous for representing new activities not known at the setup stage, for providing a quantitative measure of transitions between poses and for expressing the difference between a predicted and actual action in a numerical way.

Extracorporeal Stimulation of Sacral Nerve Roots for Observation of Pelvic Autonomic Nerve Integrity: Description of a Novel Methodological Setup.

INTRODUCTION: Neurophysiologic monitoring can improve autonomic nerve sparing during critical phases of rectal cancer surgery. OBJECTIVES: To develop a system for extracorporeal stimulation of sacral nerve roots. METHODS: Dedicated software controlled a ten-electrode stimulation array by switching between different electrode configurations and current levels. A built-in impedance and current level measurement assessed the effectiveness of current injection. Intra-anal surface electromyography (sEMG) informed on targeting the sacral nerve roots. All tests were performed on five pig specimens. RESULTS: During switching between electrode configurations, the system delivered 100% of the set current (25 mA, 30 Hz, 200 µs cathodic pulses) in 93% of 250 stimulation trains across all specimens. The impedance measured between single stimulation array contacts and corresponding anodes across all electrode configurations and specimens equaled 3.7 ± 2.5 kΩ. The intra-anal sEMG recorded a signal amplitude increase as previously observed in the literature. When the stimulation amplitude was tested in the range from 1 to 21 mA using the interconnected contacts of the stimulation array and the intra-anal anode, the impedance remained below 250 Ω and the system delivered 100% of the set current in all cases. Intra-anal sEMG showed an amplitude increase for current levels exceeding 6 mA. CONCLUSION: The system delivered stable electric current, which was proved by built-in impedance and current level measurements. Intra-anal sEMG confirmed the ability to target the branches of the autonomous nervous system originating from the sacral nerve roots. SIGNIFICANCE: Stimulation outside of the operative field during rectal cancer surgery is feasible and may improve the practicality of pelvic intraoperative neuromonitoring.

Optimal coding of vectorcardiographic sequences using spatial prediction.

This paper discusses principles, implementation details, and advantages of sequence coding algorithm applied to the compression of vectocardiograms (VCG). The main novelty of the proposed method is the automatic management of distortion distribution controlled by the local signal contents in both technical and medical aspects. As in clinical practice, the VCG loops representing P, QRS, and T waves in the three-dimensional (3-D) space are considered here as three simultaneous sequences of objects. Because of the similarity of neighboring loops, encoding the values of prediction error significantly reduces the data set volume. The residual values are de-correlated with the discrete cosine transform (DCT) and truncated at certain energy threshold. The presented method is based on the irregular temporal distribution of medical data in the signal and takes advantage of variable sampling frequency for automatically detected VCG loops. The features of the proposed algorithm are confirmed by the results of the numerical experiment carried out for a wide range of real records. The average data reduction ratio reaches a value of 8.15 while the percent root-mean-square difference (PRD) distortion ratio for the most important sections of signal does not exceed 1.1%.