Nonlinear Energy Operator Research Articles

Real-Time Recording and Processing of Spike Electrical Activity of the Small Intestine in Experiments on Rats.

Real-time recording technique and mathematical processing of the spike electrical activity in the small intestine were developed for chronic experiments on rats. Open-source software was employed to digitize electromyograms and to process them in a real-time mode with a fourth-order nonlinear differential energy operator. This method improved identification of spike electrical activity in the small intestine in experiments.

Robust Speaker Diarization Based on Daubechies Wavelet, Nonlinear Energy Operator and Pyknogram

Two common disciplines of speech processing are speaker recognition “identification and verification of speaker”, and speaker diarization, “who spoke when”. Motivated by various applications in automatic speaker recognition, speaker indexing, word counting, and audio transcription, speaker diarization (SD) becomes a significant area of signal processing. The basic designing steps of SD are feature extraction, voice activity detection (VAD), segmentation, and clustering. VAD process is accomplished by Daubechies 40, discrete wavelets transform (DWT). Initially, DWT was used for compression, scaling, and denoising of audio-stream and then partitioned into small frames of size 0.12 seconds. Next, features of each frame were extracted by applying nonlinear energy operator (NEO) based pyknogram. To measure the similarity between frames, a sliding window on delta-BIC distance metric was applied. A negative value of its output represents the same segments and vice-versa. To improve the output of the segmentation process, resegmentation was applied by information change rate method. At last, hierarchical clustering groups the homogeneous segments that correspond to a particular speaker and has been graphically represented by the dendrogram. The performance of SD was evaluated by F-measure and speaker diarization error rate (SER) and their results were compared with the traditional speaker diarization system that uses MFCC and BIC for segmentation and clustering. It reveals a significant reduction of 12.3% of SER in the proposed diarization system.

A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms.

Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.

Fault Diagnostics of Wind Turbine Drive-Train using Multivariate Signal Processing

The vibration measured from wind turbine drivetrain components is a mixture of multiple frequency modes. In practice, in wind turbine drivetrain condition monitoring systems, multiple accelerometer sensors are used to measure the vibration. Inter-channel common modes are not processed in the standard single-channel empirical mode decomposition (EMD) and it suffers from mode mixing and mode misalignment. Inter-channel correlation implies the causation of vibration mode shapes. Multivariate EMD (MEMD) possesses an enhanced spatial and spectral coherence. The mode alignment property of MEMD is used to process the inter-channel common modes, thus MEMD overcomes the limitation of mode misalignment in single-channel EMD. Still, MEMD exhibits a degree of mode mixing. White noise powers are added in separate channels to lessen the mode mixing. In this research, a novel multivariate signal processing technique, noise-assisted multivariate empirical mode signal decomposition (NA-MEMD) with a competent nonlinear Teager-Kaiser energy operator (NLTKEO), is proposed and tested for truthful extraction of instantaneous frequency and instantaneous amplitude features, and thereby ensures superior fault diagnosis performance. The dyadic filter bank structure of the proposed NA-MEMD decomposes the nonstationary vibrations effectively. The proposed method is used to predict the surface damage pattern embedded in multi-source vibrations at a low-speed planetary gear stage. The effectiveness of the proposed algorithm is tested with NREL GRC wind turbine condition monitoring benchmark datasets.

On-Line Energy-Based Milling Chatter Detection

Milling exhibits forced vibrations at tooth passing frequency and its harmonics, as well as chatter vibrations close to one of the natural modes. In addition, there are sidebands, which are spread at the multiples of tooth passing frequency above and below the chatter frequency, and make the robust chatter detection difficult. This paper presents a novel on-line chatter detection method by monitoring the vibration energy. Forced vibrations are removed from the measurements in discrete time domain using a Kalman filter. After removing all periodic components, the amplitude and frequency of chatter are searched in between the two consecutive tooth passing frequency harmonics using a nonlinear energy operator (NEO). When the energy of any chatter component grows relative to the energy of forced vibrations, the presence of chatter is detected. The proposed method works in discrete real time intervals, and can detect the chatter earlier than frequency domain-based methods, which rely on fast Fourier Transforms. The method has been experimentally validated in several milling tests using both microphone and accelerometer measurements, as well as using spindle speed and current signals.

0.6 V, 116 nW Neural Spike Acquisition IC with Self-Biased Instrumentation Amplifier and Analog Spike Extraction.

This paper presents an ultralow power 0.6 V 116 nW neural spike acquisition integrated circuit with analog spike extraction. To reduce power consumption, an ultralow power self-biased current-balanced instrumentation amplifier (IA) is proposed. The passive RC lowpass filter in the amplifier acts as both DC servo loop and self-bias circuit. The spike detector, based on an analog nonlinear energy operator consisting of a low-voltage open-loop differentiator and an open-loop gate-bulk input multiplier, is designed to emphasize the high frequency spike components nonlinearly. To reduce the spike detection error, the adjacent spike merger is also proposed. The proposed circuit achieves a low IA current consumption of 46.4 nA at 0.6 V, noise efficiency factor (NEF) of 1.81, the bandwidth from 102 Hz to 1.94 kHz, the input referred noise of 9.37 μVrms, and overall power consumption of 116 nW at 0.6 V. The proposed circuit can be used in the ultralow power spike pulses acquisition applications, including the neurofeedback systems on peripheral nerves with low neuron density.

A novel adaptive real-time detection algorithm for an area-efficient CMOS spike detector circuit

For implantable neural detection and recording systems, computationally simple methods are preferred over complex algorithms for power efficiency. Amplitude threshold is the simplest and the most widely employed method for neural spike detection. However, this conventional method for setting the threshold is sensitive to the spike firing rate and spike amplitude. In this work, a novel approach is described for a real-time calculation of the detection threshold in a way that is robust to the change in spike firing rate as well as spike amplitude. An algorithm is proposed that adaptively sets the threshold from background activity by approximating the nonlinear energy operator (NEO). The detection method can distinguish neural spikes accurately with firing rate greater than 100 Hz. The spike detector circuit dissipates 5.1 μW of power per channel and occupies 0.018 mm2 of layout area when implemented in 0.18 μm CMOS process.

Estimation of emotional arousal from speech with phase-based features

The most commonly adopted approaches in speech emotion recognition (SER) utilize magnitude spectrum and nonlinear Teager energy operator (TEO) based features while information about phase spectrum is often omitted. The information about phase has been frequently overlooked in approaches applied by speech processing researchers due to the signal processing difficulties. We present study of two phase-based features: The relative phase shift (RPS) based features and modified group delay features (MODGDF) that represents phase structure of speech in the task of emotional arousal recognition. The evaluation is performed on the CRISIS acted speech database which allows us to recognize five levels of emotional arousal from speech. To exploit these features, we employ concept of deep neural network. The efficiency of the approaches based on features mentioned earlier is compared to baseline platform using Mel frequency cepstral coefficients (MFCCs) and all pole group delay features (APGD). The combination of another phase-based types of features with our baseline platform led to the overall improvement of performance of the system for different levels of emotional arousal. These results confirm that combination of phase information and magnitude information leads to the overall improvement of performance of such system and also that combination of different types of features representing phase information brings additional increment of the performance.

Timely and efficient chatter onset detection for end-milling

Machining stability and chatter are issues that significantly limit the efficiency of metal removal processes, and for this reason they have been intensively investigated in relation to end milling and turning operations. Regardless of the advancements in machining stability prediction and modeling, there is still great need for the development of an effective chatter detection and monitoring method. Timely chatter detection prevents the damaging of the cutting tool and workpiece, as it allows for immediate intervention in the cutting process. This paper focuses on right-on-time detection of chatter onset which can be achieved by utilizing computationally efficient methods and by minimizing signal processing time lag. The first step of the proposed method is to decompose the measured vibration signal by using Gabor filter bank. Then, for the detection of the transition in cutting dynamics at the onset of chatter, the Teager’s nonlinear energy operator is used which can track variations in instantaneous frequency and amplitude. The main advantage of the energy operator is its computation efficiency with minimal time delays. The proposed method is tested on data obtained from slot end-milling of an inclined surface (incremental depth of cut) for different level of spindle speed.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

Automatic feature extraction algorithms for the assessment of in-vitro electrical recordings of rat myocardium with ablation lesions

Abstract Cardiac arrhythmias are a widely spread disease in industrialized countries. A common clinical treatment for this disease is radiofrequency ablation (RFA), in which high frequency alternating current creates a lesion on the myocardium. However, the formation of the lesion is not entirely understood. To obtain more information about ablation lesions (ALs) and their electrophysiological properties, we established an in-vitro setup to record electrical activity of rat myocardium. Electrical activity is measured by a circular shaped multielectrode array. This work was focused to gain more information by developing algorithms to process the measured electrical signals to collect different features, which may allow us to characterize an AL. First, pacing artefacts were detected and blanked. Subsequently, data were filtered. Afterwards, activations in atrial signals were detected using a non-linear energy operator (NLEO) and templates of these activations were generated. Finally, we determined different features on each activation in order to evaluate changes of unipolar as well as bipolar electrograms and considered these features before and after ablation. In conclusion, the majority of the signal features delivered significant differences between normal tissue and lesion. Among others, a reduction in peak to peak amplitude and a diminished spectral power in the band 0 to 100 Hz may be useful indicators for AL. These criteria should be verified in future studies with the aim of estimating indirectly the formation of a lesion.

Surrogate data test for nonlinearity of EEG signals: A newborn EEG burst suppression case study

This paper applies the surrogate data method to investigate the presence of nonlinearity in neonatal electroencephalogram (EEG) burst suppression (B/S) patterns in order to rationalize the use of nonlinear methods for automated detection of such patterns. To generate surrogate data, the statically transformed autoregressive process (STAP) algorithm is deployed, and, the correlation dimension (CD) and asymmetry due to time reversal (REV) are applied as discriminating statistics. The results of the surrogate data test demonstrate the nonlinearity characteristic of real neonatal EEG signals during both burst and suppression phases at the 0.05 significance level. The evidence of nonlinearity is found in 90% and 87% of bursts and suppressions respectively. Furthermore, the ability of nonlinear tools in detecting B/S patterns in multichannel neonatal EEG signals is investigated using receiver operating characteristic analysis. The experimental results show that the CD outperforms existing methods based on the nonlinear energy operator.

Interictal epileptiform discharge characteristics underlying expert interrater agreement

ObjectiveThe presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED. MethodsIED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization. ResultsMultiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11. ConclusionsThe features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer’s scores perform best with a large group of EEGers (more than 10). SignificanceBy examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a “wisdom of the crowd” effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG.

Low SNR neural spike detection using scaled energy operators for implantable brain circuits.

Real time on-chip spike detection is the first step in decoding neural spike trains in implantable brain machine interface systems. Nonlinear Energy Operator (NEO) is a transform widely used to distinguish neural spikes from background noise. In this paper we define a general form of energy operators, of which NEO is a specific example, which gives better spike-noise separation than NEO and its derivatives. This is because of a non-linear scaling applied to the general discrete energy operator. Using two well-known publically available datasets, the performance of several operators is compared. On data sets that contain multi-unit spikes with low Signal to Noise ratio, the detection accuracy was improved by approximately 15%.

Detection study of walking segments of children with cerebral-palsy based on surface electromyographic signals

In this study, surface electromyography (sEMG) of the lower limbs of cerebral-palsy (CP) subjects in gait cycle was recorded and its parameters of gait cycle characters were analyzed to assess their clinical severity. Three algorithms, including integrated profile (IP), sample-entropy (SampEN) and smooth nonlinear energy operator (SNEO) algorithm, were applied to calculate the duration of walking sEMG segments in simulated SEMG signals. After that, the efficiency and accuracy were compared among these three algorithms. SNEO was then selected as the optimal algorithm among the three algorithms and employed for real sEMG signal processing of CP subjects. The results indicated that there was no significant difference in the accuracy of sEMG segement detection for the three algorithms. However, the computation speed of SNEO algorithm was much faster than those of the others and thus it was a suitable algorithm for detecting walking sEMG segments of CP subjects. In addition, the positive correlation was found between the clinical severity and the mean duration of walking sEMG segments in CP subjects. The results indicated that there was a significant difference in the three groups of CP subjects with different levels of severity. Our findings showed that the mean duration of walking sEMG segments could be considered as an assistant index to evaluate the clinical severity of CP subjects.

A new NLEO based technique for the detection of burst–suppression patterns in multichannel neonatal EEG signals

In this paper, we propose a new method, based on the nonlinear energy operator (NLEO), to automatically detect burst---suppression (B---S) patterns in multichannel newborn electroencephalograms (EE...

A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search.

The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

Hardware Efficient Automatic Thresholding for NEO-Based Neural Spike Detection.

The nonlinear energy operator (NEO) algorithm has been commonly implemented in hardware for neural spike detection. However, the traditional method to set the threshold is sensitive to the spike firing rate. In this paper, a new approach is presented to automatically set the threshold, in real time, in a manner that is robust to the spike firing rate and suitable for a neural implant. The presented threshold calculation method statistically analyzes the neural signal standard deviation and root-mean-square frequency and can update the threshold of each channel sequentially every few seconds. Hardware efficient architectures to estimate the threshold calculation statistical parameters are also presented. This automatic thresholding method for NEO spike detection shows robust performance for firing rates from 10 to 100, occupies only 0.021mm2 in 130nm CMOS, and consumes only 50 nW in simulations with a 20-kHz clock.

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

A 0.7 V, 40 nW Compact, Current-Mode Neural Spike Detector in 65 nm CMOS.

In this paper, we describe a novel low power, compact, current-mode spike detector circuit for real-time neural recording systems where neural spikes or action potentials (AP) are of interest. Such a circuit can enable massive compression of data facilitating wireless transmission. This design can generate a high signal-to-noise ratio (SNR) output by approximating the popularly used nonlinear energy operator (NEO) through standard analog blocks. We show that a low pass filter after the NEO can be used for two functions-(i) estimate and cancel low frequency interference and (ii) estimate threshold for spike detection. The circuit is implemented in a 65 nm CMOS process and occupies 200 μm × 150 μ m of chip area. Operating from a 0.7 V power supply, it consumes about 30 nW of static power and 7 nW of dynamic power for 100 Hz input spike rate making it the lowest power consuming spike detector reported so far.