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Using a data set on individual investments in an online crowdfunding platform for mobile applications, this study examines whether an early investor’s experience within the platform serves as a credible signal of quality for other investors in the crowd and, if so, under what conditions. We find that early investors with experience—particularly investors with app development experience and investors with app investment experience—have a disproportionate influence on later investors in the crowd. Investors with app development experience are likely to have better knowledge of the product and are therefore found to be more influential for “concept apps” (apps in the pre-release stage), while investors with app investment experience with a better knowledge of market performance are found to be more influential for “live apps” (apps that are already being sold in the market). Our findings show that the majority of investors in this market, the crowd, although inexperienced, are rather sophisticated in their ability to identify and exploit nüanced differences in the underlying expertise of the early investors, informational signals that align well with the informational needs they face in the different stages of a venture. In examining the ex post performance of apps, we find that apps with investments from investors with experience are positively associated with ex post app sales. More importantly, we find that investors with experience indeed have the ability to select better apps, making their investment choices credible signals of quality for the crowd. Contrary to popular perceptions of crowdfunding platforms as substitutes for traditional expert-dominated mechanisms, our findings indicate that participation by individuals with experience can be beneficial to these markets.

Identifying transient and nonpersistent abnormal electrocardiogram (ECG) waveforms by continuously monitoring the high-risk populations is of great importance for the diagnosis, treatment, and prevention of cardiovascular diseases. In recent years, fabric electrodes have been widely used in wearable devices because of their non-irritating properties and better comfort than traditional AgCl electrodes. However, the motion noise caused by the relative movement between the fabric electrodes and skin affects the quality of ECGs and reduces the accuracy of diagnosis. Therefore, delineating the ECG waveforms that are recorded from wearable devices with varying levels of noise is still challenging. In this study, a signal quality assessment (SQA)–based ECG waveform delineation method that is used for wearable systems was developed. The ECG signal was first preprocessed by a bandpass filter. Five indices, including the multiscale nonlinear amplitude statistical distribution (adSQI1, adSQI2), the proportion of energy-related to T wave (ptSQI), and heart rates computed from R waves and T waves (rHR and tHR, respectively), were then calculated from the preprocessed ECG signal. The signals were classified as good, acceptable, and unacceptable ECGs by combining these indices through the use of a neural network. Subsequently, the R waves or/and T waves were identified for the corresponding feature interpretations based on the SQA results. ECGs that were recorded from the chest belts from 29 volunteers at different activity statuses were divided into 4-s segments. A total of 7133 manually labeled segments were used to derive (4985 segments) and validate (2148 segments) the algorithm. The adSQI1, adSQI2, tHR, and rHR characteristics were significantly different among the good, acceptable, and unacceptable ECGs. The ptSQI value was considerably higher in the good ECGs than in the acceptable and unacceptable ECGs. The ECG segments of different quality levels were classified with an accuracy of 96.74% by using the proposed SQA method. The R waves and T waves were identified with accuracies of 99.95% and 99.57%, respectively, for segments that were classified as acceptable and/or good. The SQA-based ECG waveform delineation method can perform reliable analysis and has the potential to be applied in wearable ECG systems for the early diagnosis and prevention of cardiovascular diseases.

Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the introduction of mobile and wearable health care, it is becoming increasingly important to distinguish available signals from noise. The goal of this study was to develop a signal quality assessment technology for photoplethysmogram (PPG) widely used in wearable healthcare. In this study, we developed and verified a deep neural network (DNN)-based signal quality assessment model using about 1.6 million 5-s segment length PPG big data of about 29 GB from the MIMIC III PPG waveform database. The DNN model was implemented through a 1D convolutional neural network (CNN). The number of CNN layers, number of fully connected nodes, dropout rate, batch size, and learning rate of the model were optimized through Bayesian optimization. As a result, 6 CNN layers, 1,546 fully connected layer nodes, 825 batch size, 0.2 dropout rate, and 0.002 learning rate were needed for an optimal model. Performance metrics of the result of classifying waveform quality into ‘Good’ and ‘Bad’, the accuracy, specificity, sensitivity, area under the receiver operating curve, and area under the precision–recall curve were 0.978, 0.948, 0.993, 0.985, 0.980, and 0.969, respectively. Additionally, in the case of simulated real-time application, it was confirmed that the proposed signal quality score tracked the decrease in pulse quality well. •Signal quality assessment using raw photoplethysmogram without pre-processing.•Deep learning-based photoplethysmogram quality assessment.•Validation using 30 times or more of big data compared to existing studies.•Secured high performance (0.98 of area under curve) with high reliability.

Purpose Non-invasive fetal electrocardiography (fECG) offers crucial information for assessing early diagnosis of fetal distress and morbidity. However, the non-invasive fECG signals probably contain various non-stationary noises, which may generate a bad influence on signal processing. Signal quality assessment plays a crucial role in accurate feature estimation for obtaining high-quality signals. Methods This study develops a comprehensive framework for the assessment of signal quality for non-invasive fECG signals. Firstly, the ECG collection equipment is employed to gather abdominal ECG signal data from eight pregnant women in the hospital. Secondly, signal preprocessing is operated including signal segmentation and data normalization process. Subsequently, a total of thirty-seven signal quality indexes (SQIs) are extracted which consist of the amplitude-based SQI, R-wave-based SQI, statistical-based SQI, fractal dimension SQI, power spectrum distribution-based SQI, and entropy domain-based SQI. Then, in order to reduce the dimensionality of features and improve the experimental performance, information gain is carried out to identify the subset of the optimal features. At last, the classifier combines different feature numbers to classify the quality of the non-invasive fECG signal. Results Ten classifiers are selected to perform a classification task between good-quality and bad-quality abdominal signals. The experimental results show that the combination of twenty-four effective features and random forest achieved the highest classification outcome, which in terms of the ACC, and F1 scores are 0.9508, and 0.9510, respectively. Conclusion The experimental results indicate that our work can reliably assess the signal quality for non-invasive fECG signals and filter out good-quality signals. This proposed algorithm can help to improve the accuracy of fetal signal extraction and fetal heart rate estimation for further analysis, which is beneficial to promoting fetal health monitoring.

Remote photo-plethysmography (rPPG) has emerged as a non-intrusive and promising physiological sensing capability in human–computer interface (HCI) research, gradually extending its applications in health-monitoring and clinical care contexts. With advanced machine learning models, recent datasets collected in real-world conditions have gradually enhanced the performance of rPPG methods in recovering heart-rate and heart-rate-variability metrics. However, the signal quality of reference ground-truth PPG data in existing datasets is by and large neglected, while poor-quality references negatively influence models. Here, this work introduces a new imaging blood volume pulse (iBVP) dataset of synchronized RGB and thermal infrared videos with ground-truth PPG signals from ear with their high-resolution-signal-quality labels, for the first time. Participants perform rhythmic breathing, head-movement, and stress-inducing tasks, which help reflect real-world variations in psycho-physiological states. This work conducts dense (per sample) signal-quality assessment to discard noisy segments of ground-truth and corresponding video frames. We further present a novel end-to-end machine learning framework, iBVPNet, that features an efficient and effective spatio-temporal feature aggregation for the reliable estimation of BVP signals. Finally, this work examines the feasibility of extracting BVP signals from thermal video frames, which is under-explored. The iBVP dataset and source codes are publicly available for research use.

To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.

The Quasi-Zenith Satellite System (QZSS) plays a pivotal role in providing vital navigation, positioning, timing, and signal authentication services, particularly through its L5 signal. Despite its importance, research on the performance of the L5 signal remains relatively limited. This study presents an empirical analysis of the L5 signal, identifying the distinct amplitude and phase distortion phenomena within its constellation diagram. Simulation methods are employed to replicate these observed anomalies, revealing that the L5 signal is significantly impacted by in-band inter-signal interference and the multipath effect at the satellite end of the star. A quantitative analysis is performed to investigate the underlying causes of these distortions, offering a deeper understanding of the factors contributing to the observed signal irregularities. The findings provide essential data and theoretical insights, contributing to the optimization of the QZSS signal quality and performance.

With the modernization of global navigation satellite systems (GNSS), especially the rapid development of the BeiDou Navigation Satellite System (BDS), more observations of satellites and signals have become available. Using data of the globally distributed MGEX stations, a systematic and comprehensive evaluation of signal characteristics for BDS-3, BDS-2, GPS, and Galileo is conducted in terms of carrier-to-noise ratio (C/N0), code noise, and multipath in the contribution. First, a comprehensive signal quality assessment method for BDS/Galileo/GPS satellites and signals is proposed, including C/N0 modeling and MP modeling. For BDS, the BDS-3 satellites apparently have higher signal power than the BDS-2 satellites at the same frequency such as B1I and B3I, and the signal B2a of BDS-3 is superior to other signals in regard to signal power, which is comparable with the superior Galileo E5 signals and GPS L5. Among all the signals, the observation accuracy of E5 is the highest regardless of receiver types, and next highest are BDS-3 B2a and GPS L5. Due to not being affected by the systematic code errors of BDS-2, the observations of BDS-3 satellites contain smaller multipath errors than that of BDS-2 satellites. As for the multipath suppression performance, the BDS-3 signal B2a, GPS L5, and Galileo E5 and E5b perform better than the other signals, which may be related to their wide signal bandwidths.

Spoofing interference poses a significant challenge to the Global Navigation Satellite System (GNSS). To effectively combat intermediate spoofing signals, this paper presents an enhanced spoofing detection method based on abnormal energy of the quadrature (Q) channel correlators. The detailed principle of this detection method is introduced based on the received signal model under spoofing attack. The normalization parameter used in this method was the estimation of the noise floor. The performance of the proposed Q energy detector is validated through simulations, the Texas Spoofing Test Battery dataset and field tests. The results demonstrate that the proposed detector significantly enhances detection performance compared to signal quality monitoring methods, particularly in overpowered scenarios and dynamic scenarios. By increasing the detection probability in the presence of spoofing signals and decreasing the false alarm probability in the absence of spoofing signals, the proposed detector can better meet the requirements of practical applications.

GNSS users may suffer satellite-induced elevation-dependent (SIED) ranging errors when some faults occur in the signal generation hardware onboard, as in the cases of GPS SVN-49 and BDS-II satellites. The wide-area differential corrections of SBAS will be invalidated in face of a SIED anomaly due to the various elevations of the widely distributed reference stations; thus, the integrity for safety–critical users may be damaged. Signal quality monitor (SQM) is utilized to detect potential hazardous deformations in GNSS signals and protect the integrity. However, in face of a SIED anomaly, the reference-averaging process of current SQM architecture is invalidated as well because the SQM measurements of reference stations will be elevation-dependent. We propose two new approaches to augment and enhance the current SQM method, namely reference-station-voting and metric-differencing processes, and develop a methodology of SQM algorithm design and evaluation as a support for validation of the improved SQM method. By applying GPS L1 C/A signals, we verify that the proposed new approaches of the improved SQM method as well as the designed SQM algorithms are effective. Furthermore, we prove that a hybrid SQM algorithm based on both multi-correlator and chip domain observables is able to protect the SBAS users against SIED anomalies with a performance margin of about 4 dB under the requirements of Category-I precision approach of civil aviation.