Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making

Beinecke, Jacqueline Michelle; Heider, Dominik

doi:10.1186/s13040-021-00283-6

Cited by 34 publications

(15 citation statements)

References 34 publications

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“…Upsampled patients were selected randomly from each profile and across features Gaussian noise was added to the observations (+/- 1 SD ). Upsampling with Gaussian noise was shown to be especially suitable for clinical data sets ( 45 ). As a result, no class imbalance was present for multi-class and OVO.…”

Section: Methodsmentioning

confidence: 99%

A flexible data-driven audiological patient stratification method for deriving auditory profiles

et al. 2022

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For characterizing the complexity of hearing deficits, it is important to consider di erent aspects of auditory functioning in addition to the audiogram. For this purpose, extensive test batteries have been developed aiming to cover all relevant aspects as defined by experts or model assumptions. However, as the assessment time of physicians is limited, such test batteries are often not used in clinical practice. Instead, fewer measures are used, which vary across clinics. This study aimed at proposing a flexible data-driven approach for characterizing distinct patient groups (patient stratification into auditory profiles) based on one prototypical database (N =) containing audiogram data, loudness scaling, speech tests, and anamnesis questions. To further maintain the applicability of the auditory profiles in clinical routine, we built random forest classification models based on a reduced set of audiological measures which are often available in clinics. Di erent parameterizations regarding binarization strategy, cross-validation procedure, and evaluation metric were compared to determine the optimum classification model. Our data-driven approach, involving model-based clustering, resulted in a set of patient groups, which serve as auditory profiles. The auditory profiles separate patients within certain ranges across audiological measures and are audiologically plausible. Both a normal hearing profile and profiles with varying extents of hearing impairments are defined. Further, a random forest classification model with a combination of a one-vs.-all and one-vs.-one binarization strategy, -fold cross-validation, and the kappa evaluation metric was determined as the optimal model. With the selected model, patients can be classified into of the auditory profiles with adequate precision (mean across profiles = . ) and sensitivity (mean across profiles = . ). The proposed approach, consequently, allows generating of audiologically plausible and interpretable, data-driven clinical auditory profiles, providing an e cient way of characterizing hearing deficits, while maintaining clinical applicability. The method should by design be applicable to all audiological data sets from clinics or research, and in addition be flexible to summarize Frontiers in Neurology frontiersin.org Saak et al. . /fneur. .information across databases by means of profiles, as well as to expand the approach toward aided measurements, fitting parameters, and further information from databases.

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Section: Methodsmentioning

confidence: 99%

A flexible data-driven audiological patient stratification method for deriving auditory profiles

et al. 2022

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“…Adding white or coloured noise to a weak signal can sometimes, paradoxically, increase its detectability through the process of stochastic resonance [127]. It can also be used as an up-sampling method of data augmentation to enhance classification in machine learning [128]. Different coloured and white noises can now be added to signals in CEPS, or used as stand-alone data to test how noises affect the different measures.…”

Section: Adding Noise ('Add Noise')mentioning

confidence: 99%

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy

Mayor

Steffert

Datseris

et al. 2023

Entropy

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Background: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. Methods: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190–220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). Results: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1–5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. Conclusion: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.

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“…The ADASYN algorithm is an improvement of SMOTE algorithm, which was first proposed by Haibo He in 2008. [ 13 ] Based on the data distribution, it can make an adaptive decision on the number of minority samples while minimizing the learning bias, and pay special attention to difficult‐to‐learn samples. The steps of new samples generation are as follows: Step 1: Calculate the imbalance ratio of sample category:

d = m_{normalg} / m_{normalp}

…”

Section: Preliminariesmentioning

confidence: 99%

A novel imbalanced fault diagnosis method based on area identification conditional generative adversarial networks

Zou

et al. 2023

Can J Chem Eng

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As a vital technology for ensuring the stable operation of industrial equipment, fault diagnosis has received a lot of research in recent years. Most complex industrial processes are in normal working conditions during operation, so the amount of data collected under normal working conditions is much larger than that under fault working conditions. The uneven number of samples will lead to the imbalance of datasets and make it a challenging task to assure the overall accuracy. To address the issue, an innovative imbalanced fault diagnostic approach based on area identification conditional generative adversarial networks (AICGAN) is proposed. First, considering the imbalance between normal data (majority data) and fault data (minority data), a hybrid data generation method combining over‐sampling and AICGAN generator is proposed, which effectively extends the limited minority data and overcomes the inclination to majority data to some extent. On one hand, the over‐sampling algorithm reduces the impact of dataset imbalance on the AICGAN training process by linear interpolation. On the other hand, the trainable generator can create samples similar to real samples by learning the generation principle so as to enrich the minority data information and reduce the sample stacking caused by linear synthesis. The two sample production methods complement each other. Combining the raw samples, over‐sampled samples, and samples generated by generator, a new dataset is constructed. Second, the new dataset is used to train the AICGAN discriminator. In addition, in order to generate samples with higher value, an auxiliary discrimination layer is added to the discriminator to control the pattern of generated samples. Third, the balanced dataset containing the linear synthesis samples and the samples generated by the trained generator are put into the classifier to obtain the fault diagnosis. The effectiveness of the proposed approach for fault diagnosis based on AICGAN is verified using the three‐phase flow facility (TFF) dataset and the Tennessee Eastman (TE) dataset. The experimental results demonstrate that the AICGAN‐based fault diagnosis method achieves high F1 scores on the imbalanced dataset.

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Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making

Cited by 34 publications

References 34 publications

A flexible data-driven audiological patient stratification method for deriving auditory profiles

A flexible data-driven audiological patient stratification method for deriving auditory profiles

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy

A novel imbalanced fault diagnosis method based on area identification conditional generative adversarial networks

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