Investigation of a New Weighted Averaging Method to Improve SNR of Electrocochleography Recordings

Kumaragamage, Chathura; Lithgow, Brian; Moussavi, Zahra

doi:10.1109/tbme.2015.2457412

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…If ECochG is to be used routinely in the operating room and postoperative setting, however, all patients (including those with small signals) must be analyzed. In our cohort, the previously described Gaussian weighted averaging method (34,35) showed a substantial increase in SNR of ECochG signals of all frequencies. Our calculations improved the mean SNR by 1.68 dB.…”

Section: Preprocessingmentioning

confidence: 51%

“…As preprocessing, we used the following steps: i) if present, removal of stitching artifacts, ii) application of a Gaussian weighted averaging method to increase the SNR and exclude uncorrelated epochs from further analysis, and iii) a 2nd order, forward-backward filtered Butterworth bandpass filter (cutoff frequencies 10 Hz / 5 kHz for visual analysis, and 100 Hz / 5 kHz for objective evaluation methods). To increase the SNR in our ECochG recordings, we calculated the Gaussian weighted epochs S GE ( i ) as described by Davila et al ( 34 ) and Kumaragamage et al ( 35 ). We used the following equation:…”

Section: Methodsmentioning

confidence: 99%

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Objectification of intracochlear electrocochleography using machine learning

et al. 2022

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IntroductionElectrocochleography (ECochG) measures inner ear potentials in response to acoustic stimulation. In patients with cochlear implant (CI), the technique is increasingly used to monitor residual inner ear function. So far, when analyzing ECochG potentials, the visual assessment has been the gold standard. However, visual assessment requires a high level of experience to interpret the signals. Furthermore, expert-dependent assessment leads to inconsistency and a lack of reproducibility. The aim of this study was to automate and objectify the analysis of cochlear microphonic (CM) signals in ECochG recordings.MethodsProspective cohort study including 41 implanted ears with residual hearing. We measured ECochG potentials at four different electrodes and only at stable electrode positions (after full insertion or postoperatively). When stimulating acoustically, depending on the individual residual hearing, we used three different intensity levels of pure tones (i.e., supra-, near-, and sub-threshold stimulation; 250–2,000 Hz). Our aim was to obtain ECochG potentials with differing SNRs. To objectify the detection of CM signals, we compared three different methods: correlation analysis, Hotelling's T2 test, and deep learning. We benchmarked these methods against the visual analysis of three ECochG experts.ResultsFor the visual analysis of ECochG recordings, the Fleiss' kappa value demonstrated a substantial to almost perfect agreement among the three examiners. We used the labels as ground truth to train our objectification methods. Thereby, the deep learning algorithm performed best (area under curve = 0.97, accuracy = 0.92), closely followed by Hotelling's T2 test. The correlation method slightly underperformed due to its susceptibility to noise interference.ConclusionsObjectification of ECochG signals is possible with the presented methods. Deep learning and Hotelling's T2 methods achieved excellent discrimination performance. Objective automatic analysis of CM signals enables standardized, fast, accurate, and examiner-independent evaluation of ECochG measurements.

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Section: Preprocessingmentioning

confidence: 51%

Section: Methodsmentioning

confidence: 99%

Objectification of intracochlear electrocochleography using machine learning

et al. 2022

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“…Potential applications of this data set include, but are not limited to (i) refinement and further use of the deep learning network 31 , (ii) improvement of pre-processing and SNR-enhancing algorithms and data analysis 16 , 31 – 33 , (iii) correlation of ECochG signal components and impedance measurements with hearing thresholds 15 , 16 , 21 , 22 , 34 , (iv) longitudinal evaluation and repeatability assessment of ECochG data 21 , and (v) correlation of multi-frequency and broad-band ECochGs with pure tone ECochGs and hearing thresholds 35 .…”

Section: Background and Summarymentioning

confidence: 99%

“…Data preprocessing. To pre-process ECochG signals, we implemented the following steps (see 31 for further details): i) if needed, removal of stitching artifacts; ii) application of a Gaussian-weighted averaging method adapted from 33 to remove uncorrelated epochs; and iii) application of a second-order Butterworth band-pass filter in forward-backward filtering mode (cutoff frequencies at 10 Hz/5 kHz for visual analysis, and 100 Hz/5 kHz for the objective algorithms). The SNR was calculated using the ± averaging method 38 .…”

Section: Ecochg Datamentioning

confidence: 99%

An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning

et al. 2023

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Electrocochleography (ECochG) measures electrophysiological inner ear potentials in response to acoustic stimulation. These potentials reflect the state of the inner ear and provide important information about its residual function. For cochlear implant (CI) recipients, we can measure ECochG signals directly within the cochlea using the implant electrode. We are able to perform these recordings during and at any point after implantation. However, the analysis and interpretation of ECochG signals are not trivial. To assist the scientific community, we provide our intracochlear ECochG data set, which consists of 4,924 signals recorded from 46 ears with a cochlear implant. We collected data either immediately after electrode insertion or postoperatively in subjects with residual acoustic hearing. This data descriptor aims to provide the research community access to our comprehensive electrophysiological data set and algorithms. It includes all steps from raw data acquisition to signal processing and objective analysis using Deep Learning. In addition, we collected subject demographic data, hearing thresholds, subjective loudness levels, impedance telemetry, radiographic findings, and classification of ECochG signals.

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“…It will lead to poor extraction accuracy of the human target signal, and even cause missed alarms and false alarms. Many methods to improve SCNR have been proposed, such as the weighted averaging method [30] and direct-sequence spreadspectrum demodulator statistics [31]. For UWB impulse radar, increasing the transmitting power of the radar is a simple and effective method.…”

Section: Introductionmentioning

confidence: 99%

A Method for Reducing Timing Jitter’s Impact in Through-Wall Human Detection by Ultra-Wideband Impulse Radar

Shi

Pan

et al. 2021

Remote Sensing

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Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction.

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Investigation of a New Weighted Averaging Method to Improve SNR of Electrocochleography Recordings

Cited by 6 publications

References 22 publications

Objectification of intracochlear electrocochleography using machine learning

Objectification of intracochlear electrocochleography using machine learning

An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning

A Method for Reducing Timing Jitter’s Impact in Through-Wall Human Detection by Ultra-Wideband Impulse Radar

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