A Novel Blood Pressure Estimation Method with the Combination of Long Short Term Memory Neural Network and Principal Component Analysis Based on PPG Signals

Şentürk, Ümit Kemal; Polat, Kemal; Yücedağ, İ̇brahim

doi:10.1007/978-3-030-36178-5_75

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…In the work presented in [26], a unique blood pressure estimation technique based on the feature of PPG signals using LSTM and PCA was presented. The raw PPG signals were employed to extract 12 time-domain features.…”

Section: Related Workmentioning

confidence: 99%

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Omer,

Salah,

Hassan

et al. 2024

BioMedInformatics

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One of the most significant indicators of heart and cardiovascular health is blood pressure (BP). Blood pressure (BP) has gained great attention in the last decade. Uncontrolled high blood pressure increases the risk of serious health problems, including heart attack and stroke. Recently, machine/deep learning has been leveraged for learning a BP from photoplethysmography (PPG) signals. Hence, continuous BP monitoring can be introduced, based on simple wearable contact sensors or even remotely sensed from a proper camera away from the clinical setup. However, the available training dataset imposes many limitations besides the other difficulties related to the PPG time series as high-dimensional data. This work presents beat-by-beat continuous PPG-based BP monitoring while accounting for the aforementioned limitations. For a better exploration of beats’ features, we propose to use wavelet scattering transform as a better descriptive domain to cope with the limitation of the training dataset and to help the deep learning network accurately learn the relationship between the morphological shapes of PPG beats and the BP. A long short-term memory (LSTM) network is utilized to demonstrate the superiority of the wavelet scattering transform over other domains. The learning scenarios are carried out on a beat basis where the input corresponding PPG beat is used for predicting BP in two scenarios; (1) Beat-by-beat arterial blood pressure (ABP) estimation, and (2) Beat-by-beat estimation of the systolic and diastolic blood pressure values. Different transformations are used to extract the features of the PPG beats in different domains including time, discrete cosine transform (DCT), discrete wavelet transform (DWT), and wavelet scattering transform (WST) domains. The simulation results show that using the WST domain outperforms the other domains in the sense of root mean square error (RMSE) and mean absolute error (MAE) for both of the suggested two scenarios.

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Section: Related Workmentioning

confidence: 99%

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Omer,

Salah,

Hassan

et al. 2024

BioMedInformatics

View full text Add to dashboard Cite

show abstract

“…But, it can be evaluated by measuring its impact on the accuracy of the deep-learning-based BP estimation models. Similar to [54], the long short-term memory (LSTM) model [55] is employed for training our dataset. However, in [54] the training is performed on extracted features from the PPG time domain and PCA features.…”

Section: Cleaning Evaluationmentioning

confidence: 99%

“…Similar to [54], the long short-term memory (LSTM) model [55] is employed for training our dataset. However, in [54] the training is performed on extracted features from the PPG time domain and PCA features. On the other hand, we are carrying training on the PPG data directly.…”

Section: Cleaning Evaluationmentioning

confidence: 99%

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

et al. 2022

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This work is supported in part by the Information Technology Industry Development Agency (ITIDA) under number ARP2019.R27.4.ABSTRACTA growing attention is given to exploiting Photoplethysmography (PPG) signals in noninvasively measuring many physiological vital signs. Many machine deep learning models were trained for predicting the continuous arterial blood pressure (ABP) or just the systolic and diastolic blood pressure (BP) values based on a public database. However, jointly cleaning the PPG-ABP dataset that is the most critical pre-processing step for training quality is still in need for more investigations. There is a considerable amount of anomaly data that has to be excluded before any training stage. This paper introduces a two-level joint PPG-ABP cleaning technique conducted at a signal level and per-beat level. Many quality metrics have been checked successively for excluding improper data. These metrics achieve a coarse cleaning step. Finally, principal component analysis (PCA) is exploited for fine cleaning the remaining data from the former stage. The cleaning efficiency is evaluated by measuring its impact on the deep-learning-based BP estimation models. The trained model based on our cleaned data shows performance enhancement in terms of prediction error and the correlation between the predicted and ground-truth BP. Segmented and cleaned PPG/ABP dataset will be publicly available at both signal level and beat level. Based on the simulation results, the proposed cleaning technique enhances the standard deviation of the prediction error of systolic and diastolic blood pressure by 11.68 % and 10.81 %, respectively. Also, it enhances the mean absolute error of the prediction of systolic and diastolic blood pressure by 14.79 % and 11.70 %, respectively.

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“…Son katman ise sistemin çıktısı olarak görev görür. Sinir ağlarının performansı gizli katman sayılarının ve her bir katmandaki düğüm sayılarının arttırılıp azaltılması (deneme yanılma) ile iyileştirilir [12,15,[19][20][21].…”

Section: A Makine öğRenmesi Ve Uzun Kısa Vadeli Bellek (Lstm)unclassified

LSTM tabanlı Derin Sinir Ağı ile Beşinci Nesil Küçük Hücre Ağlarında El Değiştirme Tahmini

Ci̇ci̇oğlu¹

2021

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Bu çalışmada, Uzun Kısa-Vadeli Hafıza (LSTM) tabanlı derin sinir ağı ile beşinci nesil küçük hücre ağlarında el değiştirme (handover, HO) tahminlerini gerçekleştiren yeni bir model geliştirilmiştir. İlk olarak HO tahmininde eğitim için kullanılacak olan veri seti Riverbed Modeler benzetim yazılımında tasarlanan benzetim senaryoları ile oluşturulmuştur. Bu senaryolar aracılığıyla sinir ağının veri kümesinde kullanılacak üç adet giriş (RSSI, SNR ve Jitter) değişkeni ve bir adet çıkış (istenen değer) değişkeni elde edilmiştir. Bu veri seti makine öğrenmesi algoritmalarından LSTM, SVM, Tree ve Lineer Regresyon teknikleri ile eğitilmiştir. LSTM tabanlı derin sinir ağı diğer regresyon algoritmaları ile karşılaştırılmış ve daha yüksek başarıma sahip olduğu tespit edilmiştir. LSTM için eğitilen modelin test sonuçları incelendiğinde; R 2 0.94, MAE 0.3315, MSE 0.3670 ve RMSE değeri 0.6058 olarak bulunmuştur. LSTM tabanlı derin sinir ağlarının, regresyon işlemlerinde yüksek başarım gösterdiği görülmüştür. Sonuç olarak önerilen regresyon modeli ile 5G küçük hücre ağlarında HO kararlarının tahmin edilebildiği gösterilmiştir.

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A Novel Blood Pressure Estimation Method with the Combination of Long Short Term Memory Neural Network and Principal Component Analysis Based on PPG Signals

Cited by 3 publications

References 11 publications

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

LSTM tabanlı Derin Sinir Ağı ile Beşinci Nesil Küçük Hücre Ağlarında El Değiştirme Tahmini

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