Detection Error Rate Research Articles

Using ultrasound tongue imaging to support the phonetic transcription of childhood speech sound disorders

ABSTRACT This study aims to determine whether adding an additional modality (ultrasound tongue imaging) improves the inter-rater reliability of phonetic transcription in childhood speech sound disorders (SSDs) and whether it enables the identification of different or additional errors in children’s speech. Twenty-three English speaking children aged 5–13 years with SSDs of unknown origin were recorded producing repetitions of /aCa/ for all places of articulation with simultaneous audio and ultrasound. Two types of transcriptions were undertaken off-line: (1) ultrasound-aided transcription by two ultrasound-trained speech-language pathologists (SLPs) and (2) traditional phonetic transcription from audio recordings, completed by the same two SLPs and additionally by two different SSD specialist SLPs. We classified transcriptions and errors into ten different subcategories and compared: the number of consonants identified as in error by each transcriber; the inter-rater reliability; and the relative frequencies of error types identified by the different types of transcriber. Results showed that error-detection rates were different across the transcription types, with the ultrasound-aided transcribers identifying more errors than were identified using traditional audio-only transcription. Analysis revealed that these additional errors were identified on the dynamic ultrasound image despite being transcribed as correct, suggestive of subtle motor speech differences. Interrater reliability for classifying the type of error was substantial (κ = 0.72) for the ultrasound-aided transcribers and ranged from fair to moderate for the audio-only transcribers (κ = 0.38 to 0.52). Ultrasound-aided transcribers identified more instances of increased variability and abnormal timing errors than the audio-only transcribers.

FrWT-PPCA-Based R-peak Detection for Improved Management of Healthcare System

Fourier analysis is well known to provide complete information of the frequencies present in a signal. But in the process, time information is lost. Therefore, its time-frequency representation is required for depicting both time and frequency information simultaneously. Therefore in this paper, fractional wavelet transform (FrWT) is proposed to be used for the first time for extracting the features of various datasets in a standard ECG database by combining the advantages of both fractional domain techniques and wavelets as case-II. Afterwards, Probabilistic Principal Component Analysis (PPCA) is used for detecting R-peaks for diagnosing heart abnormalities in various morphologies of the ECG signal. The proposed technique has been evaluated on the basis of sensitivity (SEN), detection error rate (DER), and positive predictivity (PPR) (of the detected ECG beats) for MIT-BIH Arrhythmia database (M/B Ar DB). Even though both FrFT and FrWT techniques exhibit a high degree of robustness, but SEN of 99.99%, DER of 0.026%, & PPR of 99.99% obtained by latter in case-II are better than SEN of 99.97%, DER of 0.053%, & PPR of 99.98% obtained by the former in case-I for M/B Ar DB. In this paper, average time error (ATE) is also obtained for the considered datasets establishing the effectiveness of the proposed technique further. These encouraging results suggest that the proposed methodology will go a long way in assisting the cardiologists to detect temporal patterns in a wide variety of electrophysiological cases, which is important for improved management of healthcare system.

A novel adaptive multilevel thresholding based algorithm for QRS detection

The heart is the single most important organ of the human body. By studying the ECG signal any abnormalities in the heart’s tempo can be identified. The QRS complex is the most prominent feature of an ECG signal. However, the detection of this feature is hampered by the presence of high and low-frequency noises in the ECG signal and abrupt changes in the signal’s morphology. This paper presents a new method of QRS detection using advanced adaptive multilevel thresholding (AAMT) with selective statistical false peak elimination (SSFPE). Firstly, a band-pass filter is used to filter out most of the unwanted noise and interference to aid the detection operation. Then, AAMT is applied to the entire ECG record to find the location and amplitudes of the pseudo peaks. Next, SSFPE is employed to eliminate false peaks resulting from electromyogram (EMG) and any other high-frequency noise that has not been eliminated in the filtering stage. Finally, after most of the peaks are correctly identified a search back stage is included to find any low amplitude true peaks that might have been missed in the peak detection stage. The proposed method is tested on the MIT-BIH arrhythmia and Fantasia databases and shows high accuracy in detection compared to many state-of-the-art QRS detection methods. The method yields high sensitivity, positive predictivity, and a low detection error rate of 99.85%, 99.91%, and 0.25%, respectively, for the MIT-BIH arrhythmia and 99.98%, 99.90%, and 0.12%, respectively for the Fantasia database.

Survey on Deep Learning Based Intrusion Detection System

Development of computer network has changed human lives in many ways. Currently, everyone is connected to each other from everywhere. Information can be accessed easily. This massive development has to be followed by good security system. Intrusion Detection System is important device in network security which capable of monitoring hardware and software in computer network. Many researchers have developed Intrusion Detection System continuously and have faced many challenges, for instance: low detection of accuracy, emergence of new types malicious traffic and error detection rate. Researchers have tried to overcome these problems in many ways, one of them is using Deep Learning which is a branch of Machine Learning for developing Intrusion Detection System and it will be discussed in this paper. Machine Learning itself is a branch of Artificial Intelligence which is growing rapidly in the moment. Several researches have showed that Machine Learning and Deep Learning provide very promising results for developing Intrusion Detection System. This paper will present an overview about Intrusion Detection System in general, Deep Learning model which is often used by researchers, available datasets and challenges which will be faced ahead by researchers

Are gamma passing rate and dose-volume histogram QA metrics correlated?

The quality of a measured distribution of dose delivered against its corresponding radiotherapy plan is routinely assessed by gamma index (GI) and dose-volume histogram (DVH) metrics. Any correlation between error detection rates, as based on either of these approaches, while argued, has never been convincingly demonstrated. The dependence of the strength of correlation between the GI passing rate ( ) and DVH quality assurance (QA) metrics on various elements of the therapy plan has not been systematically investigated. A formal analysis of the relation between and DVH metrics has been undertaken, leading to a relationship which may partly approximate with respect to the DVH. This relationship was further validated by studying examples of simulated clinical radiotherapy plans and by studying the correlation between and the derived relationship using a simple two-dimensional representations of the planning target volume (PTV) and organs at risk (OAR), where penumbra regions, distance-to-agreement tolerances and dose delivery errors were systematically varied. It is shown formally that there cannot be any correlation between and other commonly applied DVH-derived QA measures. However, may be partly approximated given the planned and measured DVH. The derived approximation (the " -slope indicator") may be clinically useful in some practical cases of radiotherapy plan QA. In formal terms, there cannot be any correlation between and any common DVH-calculated patient-specific measures, with respect to PTV or OAR. However, as demonstrated analytically and further confirmed in our simulation studies, the approximation derived in this study (the " -slope indicator") may in some cases offer a degree of correlation between and the PTV and OAR DVH QA metrics in measured and planned patient-specific dose distributions-which may be potentially useful in clinical practice.

Research on the Method of Predicting Fractional Flow Reserve Based on Multiple Independent Risk Factors.

The use of diameter stenosis (DS), as revealed by coronary angiography, for predicting fractional flow reserve (FFR) usually results in a high error rate of detection. In this study, we investigated a method for predicting FFR in patients with coronary stenosis based on multiple independent risk factors. The aim of the study was to improve the accuracy of detection. First, we searched the existing literature to identify multiple independent risk factors and then calculated the corresponding odds ratios. The improved analytic hierarchy process (IAHP) was then used to determine the weighted value of each independent risk factor, based on the corresponding odds ratio. Next, we developed a novel method, based on the top seven independent risk factors with the highest weighted values, to predict FFR. This model was then used to predict the FFR of 253 patients with coronary stenosis, and the results were then compared with previous methods (DS alone and a simplified scoring system). In addition to DS, we identified a range of other independent risk factors, with the highest weighted values, for predicting FFR, including gender, body mass index, location of stenosis, type of coronary artery distribution, left ventricular ejection fraction, and left myocardial mass. The area under the receiver-operating characteristic curve (AUC) for the newly developed method was 84.3% (95% CI: 79.2–89.4%), which was larger than 65.3% (95% CI: 61.5–69.1%) of DS alone and 74.8% (95% CI: 68.4–81.2%) of the existing simplified scoring system. The newly developed method, based on multiple independent risk factors, effectively improves the prediction accuracy for FFR.

Neural correlates of metacognition across the adult lifespan

Metacognitive accuracy describes the degree of overlap between the subjective perception of one's decision accuracy (i.e. confidence) and objectively observed performance. With older age, the need for accurate metacognitive evaluation increases; however, error detection rates typically decrease. We investigated the effect of ageing on metacognitive accuracy using event-related potentials (ERPs) reflecting error detection and confidence: the error/correct negativity (Ne/c) and the error/correct positivity (Pe/c). Sixty-five healthy adults (20 to 76 years) completed a complex Flanker task and provided confidence ratings. We found that metacognitive accuracy declined with age beyond the expected decline in task performance, while the adaptive adjustment of behaviour was well preserved. Pe amplitudes following errors varied by confidence rating, but they did not mirror the reduction in metacognitive accuracy. Ne amplitudes decreased with age for low confidence errors. The results suggest that age-related difficulties in metacognitive evaluation could be related to an impaired integration of decision accuracy and confidence information processing. Ultimately, training the metacognitive evaluation of fundamental decisions in older adults might constitute a promising endeavour.

Data Mining-based Fault Detection Method for Distributed Power Generation Systems

Traditional fault detection methods for power generation systems use cen-tralized fault processing analysis, which leads to long accuracy and responsetime of fault detection. To address these problems, a data mining-baseddistributed power generation system fault artificial intelligence detectionmethod is studied. The depth-first search tree algorithm is used to divide thegrid of distributed generation system. The network structure is modified tolocate fault zones by processing anomaly mining of the system data aftergrid division. The combination of fuzzy logic and wavelet singular entropyis used to complete the detection and identification of system faults. Throughsimulation experiments, it is verified that the response time of the detectionmethod is only 0.016 s, and its detection error rate and false negative rate are1.23% and 1.25%, which are far lower than other methods.

Algorithmic extraction of smartphone accelerometer-derived mechano-biological descriptors of resistance exercise is robust to changes in intensity and velocity.

BackgroundIt was shown that single repetition, contraction-phase specific and total time-under-tension (TUT) can be extracted reliably and validly from smartphone accelerometer-derived data of resistance exercise machines using user-determined resistance exercise velocities at 60% one repetition maximum (1-RM). However, it remained unclear how robust the extraction of these mechano-biological descriptors is over a wide range of movement velocities (slow- versus fast-movement velocity) and intensities (30% 1-RM versus 80% 1-RM) that reflect the interindividual variability during resistance exercise.ObjectiveIn this work, we examined whether the manipulation of velocity or intensity would disrupt an algorithmic extraction of single repetitions, contraction-phase specific and total TUT.MethodsTwenty-seven participants performed four sets of three repetitions of their 30% and 80% 1-RM with velocities of 1 s, 2 s, 6 s and 8 s per repetition, respectively. An algorithm extracted the number of repetitions, single repetition, contraction-phase specific and total TUT. All exercises were video-recorded. The video recordings served as the gold standard to which algorithmically-derived TUT was compared. The agreement between the methods was examined using Limits of Agreement (LoA). The Pearson correlation coefficients were used to calculate the association, and the intraclass correlation coefficient (ICC 2.1) examined the interrater reliability.ResultsThe calculated error rate for the algorithmic detection of the number of single repetitions derived from two smartphones accelerometers was 1.9%. The comparison between algorithmically-derived, contraction-phase specific TUT against video, revealed a high degree of correlation (r > 0.94) for both exercise machines. The agreement between the two methods was high on both exercise machines, intensities and velocities and was as follows:LoA ranged from -0.21 to 0.22 seconds for single repetition TUT (2.57% of mean TUT), from -0.24 to 0.22 seconds for concentric contraction TUT (6.25% of mean TUT), from -0.22 to 0.24 seconds for eccentric contraction TUT (5.52% of mean TUT) and from -1.97 to 1.00 seconds for total TUT (5.13% of mean TUT). Interrater reliability for single repetition, contraction-phase specific TUT was high (ICC > 0.99).ConclusionNeither intensity nor velocity disrupts the proposed algorithmic data extraction approach. Therefore, smartphone accelerometers can be used to extract scientific mechano-biological descriptors of dynamic resistance exercise with intensities ranging from 30% to 80% of the 1-RM with velocities ranging from 1 s to 8 s per repetition, respectively, thus making this simple method a reliable tool for resistance exercise mechano-biological descriptors extraction.

A deep neural network approach to QRS detection using autoencoders

Objective:In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase. Methods:A simple architecture has been deeply trained on many datasets to ensure the generalization of the network at inference. Results:The proposed method achieved a QRS detection accuracy of 99.6% using more than 1042000 beats which is competitive with all state-of-the-art QRS detectors. Moreover, the proposed method produced only 0.82% of Detection Error Rate using six unseen datasets containing more than 1470000 beats. Thus confirms the high performance of our method to detect QRSs. Conclusion:Stacked autoencoder neural networks are very effective in QRS detection. At inference, our algorithm processes 1042309 beats in less than 25.32 s. Thus, it is favorably comparable with state-of-the-art deep learning methods. Significance:The stacked autoencoder is an efficient tool for QRS detection, which could replace conventional systems to help practitioners make fast and accurate decisions.

Intelligent Reflecting Surface (IRS)-Aided Covert Communication With Warden’s Statistical CSI

This work considers covert communications with the aid of an intelligent reflecting surface (IRS). We jointly design the transmit power and IRS's reflection phase shifts and amplitudes to enhance covert communication performance. To this end, we first derive the total detection error rate at a warden. We then determine the IRS's optimal phase shifts and develop an effective one-dimensional search method to identify the IRS's optimal amplitudes. We show that the IRS can significantly improve the achievable covertness. In addition, optimizing the IRS's amplitudes on top of its phase shifts significantly outperforms fixing them as unit in covert communications.

An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers

With the rapid growth and advancement in technology, we are becoming more and more dependent on the internet for our day-to-day work. As a result of this, we have become an easy target for the attackers. More the usage of the internet more we are vulnerable to threats. In this scenario, the need for anti-virus or some system that can help in detecting the threats or attacks is also growing rapidly. The answer to this problem is a system that when installed on a network shall be able to detect Intrusions of any sort. Such a system can be called an IDS (Intrusion Detection System). This system is deployed on any network to keep a track of the traffic and to monitor any kind of mutation or deviations from regular traffic patterns. Many kinds of research are still ongoing in this field to develop a system that has not only a better error detection rate but also can introduce preventive measures as soon as a threat is detected. This paper proposes a system for detecting the attacks using machine learning where the Recursive Feature Elimination (RFE) technique is applied when the irrelevant features are there in the dataset. This technique helps remove any sort of redundancy in the KDD CUP99 dataset which is a standard dataset for network security and intrusion detection. Then for the achieved set of features a confusion matrix is generated and the classification of records is done using decision tree, support vector machine, and an ensemble classifier random forest in the form of discriminant analysis. When compared with other methodologies our approach holds a good classification rate for all the classes of attacks of the KDD CUP 99 dataset.

Impact of combining data from multiple instruments on performance of patient-based real-time quality control.

IntroductionIt is unclear what is the best strategy for applying patient-based real-time quality control (PBRTQC) algorithm in the presence of multiple instruments. This simulation study compared the error detection capability of applying PBRTQC algorithms for instruments individually and in combination using serum sodium as an example.Materials and methodsFour sets of random serum sodium measurements were generated with differing means and standard deviations to represent four simulated instruments. Moving median with winsorization was selected as the PBRTQC algorithm. The PBRTQC parameters (block size and control limits) were optimized and applied to the four simulated laboratory data sets individually and in combination.ResultsWhen the PBRTQC algorithm were individually optimized and applied to the data of the individual simulated instruments, it was able to detect bias several folds faster than when they were combined. Similarly, the individually applied algorithms had perfect error detection rates across different magnitudes of bias, whereas the error detection rates of the algorithm applied on the combined data missed smaller biases. The performance of the individually applied PBRTQC algorithm performed more consistently among the simulated instruments compared to when the data were combined.DiscussionWhile combining data from different instruments can increase the data stream and hence, increase the speed of error detection, it may widen the control limits and compromising the probability of error detection. The presence of multiple instruments in the data stream may dilute the effect of the error when it only affects a selected instrument.

Design and Implementation of Portable Auto Defibrillator

Sudden Cardiac Arrest (SCA) is a case that made the heart stop pumping the blood. Ventricular fibrillation (VF) and ventricular tachycardia (VT) are the major causes that lead to cardiac arrest. To treat SCA, a defibrillator device is used to return the heart to its normal sinus rhythm. The main generation of the defibrillator is the manual type which has many limitations involved., It requires the physician’s instruction to determine the patient state and the amount of joule required; therefore, it is preferable to design an auto defibrillator. The aim of this paper is to design a hardware implementation system of auto dc-shock utilized in the emergency case for diagnosing patient case and recovering the normal state of the heart. This prototype consists of five main parts, including the diagnosing system, high voltage section, charging unit, discharging unit, and safety unit. The output of diagnosing system is followed by charging and discharging period in order to get a suitable amount of joule according to patient situation of 50, 100, 120, and 150 joule discharged on the patient body by using a biphasic system provided by safety mode circuit so that the discharge process automatically happens in the case of not discharged on the patient body. The proposed diagnosing system in hardware design was tested on several patients in Ibn al-Nafis hospital and healthy patients at home and the accuracy was achieved of 97.03%, specificity is 97.8%, and the detection error rate is 3.09%.

Covert Communication Achieved by a Full-Duplex Multi-Antenna Receiver in Wireless Networks

In this paper, we consider a covert communication system with a multi-antenna full-duplex (FD) receiver to enhance covert performance. More precisely, the receiver Bob (i.e., multiple antennas) selects the best antenna to receive the covert message, and then it sends artificial noise (AN) to the warden Willie causing uncertainty by utilizing an antenna among the remaining antennas. In order to take full advantage of multi-antenna technology, we consider two cases: (1) Willie does not know which antenna Bob chooses to send AN, and Willie knows only part of the channel information. (2) Willie knows which antenna Bob chooses to send AN, and Willie knows all the channel information. Based on the analysis of the optimal detection threshold, we derive the minimum detection error probability of the two cases. Furthermore, given the pre-determined convert constraints, the closed-form expression of the maximum effective convert rate is derived. When the power of AN sent by Bob is greater than that of covert message sent by the sender Alice, surprised research results are shown: (1) the detection error rate of case 1 is higher than that of case 2. (2) the effective covert rate of case 1 is always lower than that of case 2. The simulation results show that the proposed scheme is more conducive to sending covert messages for Alice; in addition, the numerical results imply that the maximum effective covert rate of the system using the multi-antenna FD receiver is significantly higher than that of the traditional dual-antenna FD receiver system.

A hybrid EMD-DWT based algorithm for detection of QRS complex in electrocardiogram signal

Accurate QRS detection is an important first step for almost every electrocardiogram (ECG) signal analysis. However, detecting QRS is difficult, not only because of the large variety, but also as a result of interference caused by various types of noise. This paper employs a hybrid feature extraction technique of ECG signal for the detection of cardiac abnormalities. Noise removal and accurate QRS detection play a major role in the analysis of ECG signals. In this paper various types of noises such as additive white Gaussian noise, baseline wander and power line interference is eliminated to enhance the signal quality. This study proposes an improved QRS complex detection algorithm based on the combination of empirical mode decomposition-discrete wavelet transform (EMD-DWT) with threshold and compared with ordinary discrete wavelet transform. The system efficacy and performance have been evaluated using accuracy, sensitivity (Se), positive predictive value (PPV) and detection error rate (DER). The results show the high accuracy of the proposed EMD-DWT algorithm, which attains a detection error rate of 1.1233%, a sensitivity of 99.28%, and a positive predictive value of 99.99%, evaluated using the MIT-BIH arrhythmia database. The proposed algorithm improves the accuracy of QRS detection compared to state-of-art methods.

Intrusion detection for network based cloud computing by custom RC-NN and optimization

Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.

A robust QRS detection approach using stationary wavelet transform

Accurate QRS detection is crucial for reliable ECG signal analysis and the development of automatic diagnosis tools. In this paper, we propose a simple yet efficient new algorithm for QRS detection using the Stationary Wavelet Transform (SWT). The wavelet transform has been extensively exploited for QRS detection and proved to be an efficient mathematical tool for scale analysis; it provides good frequency components estimation for the input signal and has good localization capability. The proposed procedure exploits solely the first level approximation coefficients of the wavelet transform applied to the bandpass-filtered ECG signal. Therefore, it resulted in a reduced complexity algorithm compared to the existing methods which use many decomposition levels. Thresholding has been implemented using the Pan-Tompkins procedure which is known to be very powerful. Our approach has been assessed over the MIT/BIH benchmark database, the MIT noise stress test database for noise robustness evaluation and the European ST-T database. The obtained results show competitive performance with state-of-the-art algorithms. The proposed scheme achieved a sensitivity of 99.83%, a positive predictivity of 99.94% and a detection error rate of 0.228% using Lead I MIT-BIH Database, this performance is one of the best results over this benchmark, and 99.35% of sensitivity, 99.76% of positive predictivity and detection error rate of 0.9% using the European ST-T Database, hence, our algorithm achieved high performance on Holter environment. Using the MIT noise stress test database, our algorithm achieved 98.77% of sensitivity, 91.01% of positive predictivity, and 10.12% of DER. Thus, our algorithm is robust and outperforms state-of-the-art algorithms on noisy recordings.

Nested code division multiple access for distance measuring equipment interference mitigation in L‐band digital aeronautical communication system

SummaryAir‐to‐ground (A/G) communications have been transferred from the congested very high frequency (VHF) band to occupy the guard bands between adjacent distance measurement equipment (DME) navigation channels within the L‐Band. However, the inlayed channels, called L‐band digital aeronautical communication system (LDACS) channels, experience severe interference with the coexisting DME channels. In this paper, a DME interference detection and mitigation technique is proposed to improve the performance of the LDACS in the presence of DME interference. The DME energy detector based on Neyman–Pearson test is proposed, which has more advantages than the other techniques due to its noncoherent nature and simplicity. Furthermore, the proposed DME detection technique is performed by using an adaptive threshold to achieve the best trade‐off between the DME signal detection and false alarm. The proposed DME interference mitigation technique relies on the proposed concept of nested RF upconversion, where the in‐phase and the quadrature components of an LDACS signal are forwarded to a code division multiple access (CDMA) block in which they are transmitted only over the quadrature branch once DME interfering signal is detected. In this work, the closed form expression for the probability of DME detection, false alarm, and the detection error rate are derived. Simulation is performed, and the results show that the proposed technique provides improvement in the probability of DME detection and the detection error rate compared to the previous work. Also, simulation results shows that the proposed technique provides lower bit error rate and less complexity compared to the previous work.

MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge

Advanced communication and information technologies enable smart grids to be more intelligent and automated, although many security issues are emerging. Security situational awareness (SSA) has been envisioned as a potential approach to provide safe services for power systems’ operation. However, in the power cloud master station mode, massive heterogeneous power terminals make SSA complicated, and failure information cannot be promptly delivered. Moreover, the dynamic and continuous situational space also increases the challenges of SSA. By taking advantages of edge intelligence, this paper introduces edge computing between terminals and the cloud to address the drawbacks of the traditional power cloud paradigm. Moreover, a deep reinforcement learning algorithm based on the edge computing paradigm of multiagent deep deterministic policy gradient (MADDPG) is proposed. The minimum processing cost under the premise of minimum detection error rate is taken to analyze the smart grids’ SSA. Performance evaluations show that the algorithm under this paradigm can achieve faster convergence and the optimal goal, namely the provision of real-time protection for smart grids.