A smart approach to EMG envelope extraction and powerful denoising for human–machine interfaces

Esposito, Daniele; Centracchio, Jessica; Bifulco, Paolo; Andreozzi, Emilio

doi:10.1038/s41598-023-33319-4

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…In Step 2, the EMG noise is obtained by applying Butterworth high-pass filter with a cutoff frequency of 10 Hz to the difference signal. The cutoff frequency is chosen based on the spectrum strength of the EMG signal, which is negligible under 10 Hz [19] [22]. At the end of the IRM block, the tentative EMG noise approximation (Fig.…”

Section: A Irm Methodsmentioning

confidence: 99%

A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals

Atanasoski,

Petrović,

Maneski

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Clinical interpretation of an electrocardiogram (ECG) can be detrimentally affected by noise. Removal of the electromyographic (EMG) noise is particularly challenging due to its spectral overlap with the QRS complex. The existing EMG-denoising algorithms often distort signal morphology, thus obscuring diagnostically relevant information. Methods: Here, a new iterative regeneration method (IRM) for efficient EMG-noise suppression is proposed. The main hypothesis is that the temporary removal of the dominant ECG components enables extraction of the noise with the minimum alteration to the signal. The method is validated on SimEMG database of simultaneously recorded reference and noisy signals, MIT-BIH arrhythmia database and synthesized ECG signals, both with the noise from MIT Noise Stress Test Database. Results: IRM denoising and morphology-preserving performance is superior to the wavelet-and FIR-based benchmark methods. Conclusions: IRM is reliable, computationally non-intensive, fast and applicable to any number of ECG channels recorded by mobile or standard ECG devices.

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Section: A Irm Methodsmentioning

confidence: 99%

A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals

Atanasoski,

Petrović,

Maneski

et al. 2024

IEEE Open J. Eng. Med. Biol.

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show abstract

“…Meanwhile, compared with raw signal, processing envelope of EMG reduces the computational complexity, which is beneficial for low-cost and real-time HMI. [36][37][38] Our proposed inverter was able to capture the changes of the envelope and amplify it to several hundred mV as shown in Figure 5c. The amplitude reaches around a peak-to-peak variation of 0.35 V within 5 s of muscle contraction, which is larger than half of V dd .…”

Section: High Gain Complementary Inverter As Voltage Amplifier For Em...mentioning

confidence: 99%

Highly Stable Ladder‐Type Conjugated Polymer Based Organic Electrochemical Transistors for Low Power and Signal Processing‐Free Surface Electromyogram Triggered Robotic Hand Control

Zhou,

Wu,

Tam

et al. 2023

Adv Funct Materials

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Organic electrochemical transistors (OECTs) based complementary inverters have been considered as promising candidates in electrophysiological amplification, owing to their low power consumption, and high gain. To create complementary inverters, it is important to use highly stable p‐type and n‐type polymers with well‐balanced current. In this study, the electrochemical stability of p‐type ladder‐conjugated polymer‐based OECT is improved through an annealing process that maintains its doped‐state drain current from 76% to 105% after 4,500 cycles in ambient environment. Next an OECT‐based complementary inverter made from p‐type and n‐type ladder‐conjugated polymers (PBBTL and BBL) that possess ultra‐low power consumption (≈170 nW), high gain (67 V/V), and high noise margin (92%) with full rail‐to‐rail swing, is presented. Furthermore, its potential for amplifying the envelope of surface electromyography (EMG) for robotic hand control is demonstrated. The high variation in the output (0.35 V) allows the amplified EMG signals to be directly captured by a commercial analog‐to‐digital converter, which in turn controls the robot hand to grasp different objects with low delay and low noise. These results demonstrate the capability of OECT inverter‐based amplifier in future signal processing‐free human‐machine interface, particularly useful for prosthetic control and gesture control applications.

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Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare.

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A smart approach to EMG envelope extraction and powerful denoising for human–machine interfaces

Cited by 6 publications

References 42 publications

A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals

A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals

Highly Stable Ladder‐Type Conjugated Polymer Based Organic Electrochemical Transistors for Low Power and Signal Processing‐Free Surface Electromyogram Triggered Robotic Hand Control

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

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