A signal processing framework for multimodal cardiac analysis

Hikmah, Nada Fitrieyatul; Arifin, Achmad; Sardjono, Tri Arief; Suprayitno, Eko Agus

doi:10.1109/isitia.2015.7219966

Cited by 8 publications

(10 citation statements)

References 5 publications

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“…In the table shows the results of sensor reading of non-invasive blood sugar measuring instrument (MAX30100) on the standard instrument of the Invasive blood sugar measuring instrument industry, its accuracy reaches 96.7% -98.9% with a standard deviation of 1.2 mg / dL -3.6 mg / dL. This shows that the accuracy of non-invasive sugar content measuring instruments is quite good, and its value is stable close to the value of industry standard measuring instruments [3]. Biologically, high blood sugar levels are caused by a body condition that does not have enough insulin and it causes insulin resistance, which is a hormone released by pancreas [4].…”

Section: Fig 1 Block Diagram Of a System For Measuring Blood Sugarmentioning

confidence: 69%

Design of Instrumentation in Detecting Blood Sugar Levels with Non-Invasive Technique Base on IoT (Internet of Things)

Suprayitno¹,

Setiawan²,

Dijaya³

2018

IJET

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Diabetes mellitus is a disease with metabolic disorders that result from defects in insulin secretion, which results in hyperglycemia with impaired carbohydrates, fats and body proteins. Diabetes mellitus can be known by measuring blood sugar levels which are generally done by injuring the body (Invasive) for blood. Normal blood sugar values range from 70 - 200 mg/dL. For people with diabetes mellitus, checking blood sugar is an important thing to do to maintain their body condition. In this study made Instrumentation detection of blood sugar levels non-invasive (does not hurt the body). This study uses the MAX30100 sensor as a non-invasive measure of blood sugar levels, because to determine blood sugar levels is only done by attaching the index finger to the sensor, and does not hurt the fingers. The value of blood sugar levels in this instrumentation is displayed on LCD and Android smartphones. For serial communication, Instrumentation with Android smartphones is done with IoT (Internet Of Things) technology to make it easier for medical officers to monitor the condition of diabetics through remote Android smartphones. From the results of testing Non-invasive blood sugar detection instrumentation with industry standard blood sugar measurement (Invasive), the accuracy of blood sugar readings was 90.3% with a deviation of 1.2 - 39.6 mg/dL. Tests were carried out on 14 Normal patients and people with diabetes mellitus. Reading distance reading Non-invasive blood sugar detection instrumentation with an Android Smartphone can reach a distance of 823 Km as long as Instrumentation is connected to the internet. Tests have been carried out in several cities including Sidoarjo, North Jakarta, Sidoarjo-Lamongan, Sidoarjo-Kediri.

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Section: Fig 1 Block Diagram Of a System For Measuring Blood Sugarmentioning

confidence: 69%

Design of Instrumentation in Detecting Blood Sugar Levels with Non-Invasive Technique Base on IoT (Internet of Things)

Suprayitno¹,

Setiawan²,

Dijaya³

2018

IJET

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“…ECG signal data retrieval was recorded for 5 minutes. The QRS complex detection algorithm applied the framework from previous research [33]. The ECG signal was pre-filtered to reduce muscle noise.…”

Section: Ecg Signal Processingmentioning

confidence: 99%

“…The main contribution of this study was to develop an algorithm to assess sleep quality by combining ECG and EMG signals using machine learning. In the field of biomedical engineering, machine learning is commonly utilized for classification in applications such as muscular fatigue during exercises [28], multimodal cardiac analysis [29], and classification for liver fibrosis prediction [30].…”

Section: Introductionmentioning

confidence: 99%

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

2023

IJIES

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Poor sleep quality is a common sign of a variety of sleep and health problems. Thus, sleep quality assessment is necessary as it can be a first-step predictor of physical and mental health. Several studies were completed for this objective. However, no prior study in sleep quality assessment has explored a comprehensive heart rate variability (HRV) analysis by including feature extraction in the time and frequency domain, and nonlinear analysis. This study proposed a full evaluation of sleep quality, by incorporating multiple physiological signs in subjects to detect exhaustion after a period of sleep. The primary contribution was the development of an algorithm to estimate sleep quality based on the combination of electrocardiography (ECG) and electromyography (EMG) signals by using machine learning. HRV analysis of ECG signal included time domain, frequency domain, and non-linear analysis. Mean power frequency (MPF) was extracted from the EMG signal by using spectral analysis. In addition, determination of fatigue level as an indicator of the subject's sleep quality was evaluated with fatigue severity scale (FSS) questionnaire. Based on results, the accuracy values of logistic regression, random forest, knearest neighbor (k-NN), support vector machine (SVM), and SVM with grid-search were 93%, 93%, 93%, 40%, and 100%, respectively. The proposed method was applicable for investigating sleep quality.

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“…After the band pass filter with gain circuit, the next step is filtering. Multilevel filtering is also used in [12]. With multilevel filtering, unwanted noise will be suppressed.…”

Section: E Band Pass Filter With Gainmentioning

confidence: 99%

Comparison of Amplifiers Utilization in Instrumentation to Record Muscle Signals in the Neck for Electrolarynx Applications

Fatoni

Ashriyah²,

Sardjono

et al. 2022

JAREE

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Electrolarynx is an assistive technology commonly used by speech impaired people to speak. The speech impaired people who have lost their larynx (laryngectomee) or have damaged larynx use an electrolarynx device to be able to speak again. The use of the electrolarynx is generally equipped with a button to turn on and start the generation of sound from the electrolarynx. Several studies have tried to use other control methods by using the muscles in the neck, namely the sternohyoid muscle. Activation of the sternohyoid muscle has an influence on sound formation. The sternohyoid muscle is a small and long muscle so recording EMG (electromyograph) signals from this muscle is quite difficult. If the recording process of this signal can be carried out properly, then the electrolarynx control by empowering this muscle will be another solution in using the electrolarynx. In this study, instrumentation amplifier which is an important stage of recording EMG signals of neck muscle was tested and compared. There are two types of instrumentation amplifier tested. The first instrumentation amplifier uses a single IC from IC AD620 while the other is a combination circuit of IC OP07. The EMG signal in the subject's neck muscles was then recorded using the instrumentation amplifiers. The subject will sit down and pronounce the vowels "a", and "i". From the testing process, it was found that the average gain on IC AD620 (minimum 1.74362 volts and maximum 3.70538 volts) was greater than the gain on IC OP07 (minimum 0.57779 volts and maximum 1.71190 volts). IC AD620 also has an overall use area of 5.61 cm2 with the use of 4 external components. Thus, it can be concluded that the best instrumentation amplifier for recording EMG neck muscle for electrolarynx application is by using IC AD620.

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A signal processing framework for multimodal cardiac analysis

Cited by 8 publications

References 5 publications

Design of Instrumentation in Detecting Blood Sugar Levels with Non-Invasive Technique Base on IoT (Internet of Things)

Design of Instrumentation in Detecting Blood Sugar Levels with Non-Invasive Technique Base on IoT (Internet of Things)

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

Comparison of Amplifiers Utilization in Instrumentation to Record Muscle Signals in the Neck for Electrolarynx Applications

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