Adenylate kinase phosphate energy shuttle underlies energetic communication in flagellar axonemes.

The complexities of energy transfer mechanisms in the flagella of mammalian sperm flagella have been intensively investigated and demonstrate significant diversity across species. Enzymatic shuttles, particularly adenylate kinase (AK) and creatine kinase (CK), are pivotal in the efficient transfer of intracellular ATP, showing distinct tissue- and species-specificity. Here, the expression profiles of AK and CK were investigated in mice and found to fall into four subgroups, of which Subgroup III AKs were observed to be unique to the male reproductive system and conserved across chordates. Both AK8 and AK9 were found to be indispensable to male reproduction after analysis of an infertile male cohort. Knockout mouse models showed that AK8 and AK9 were central to promoting sperm motility. Immunoprecipitation combined with mass spectrometry revealed that AK8 and AK9 interact with the radial spoke (RS) of the axoneme. Examination of various human and mouse sperm samples with substructural damage, including the presence of multiple RS subunits, showed that the head of radial spoke 3 acts as an adapter for AK9 in the flagellar axoneme. Using an ATP probe together with metabolomic analysis, it was found that AK8 and AK9 cooperatively regulated ATP transfer in the axoneme, and were concentrated at sites associated with energy consumption in the flagellum. These findings indicate a novel function for RS beyond its structural role, namely, the regulation of ATP transfer. In conclusion, the results expand the functional spectrum of AK proteins and suggest a fresh model regarding ATP transfer within mammalian flagella.

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.

Establishment and Validation of Novel Prognostic Subtypes in Hepatocellular Carcinoma Based on Bile Acid Metabolism Gene Signatures Using Bulk and Single-Cell RNA-Seq Data.

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Erratum: [Corrigendum] Cross regulation of signaling pathways in gastrointestinal stromal tumor.

[This corrects the article DOI: 10.3892/ol.2018.9494.].

Effects of 13C isotope-labeled allelochemicals on the growth of the invasive plant Alternanthera philoxeroides.

The secondary metabolites of indigenous plants have significant allelopathic inhibitory effects on the growth and development of invasive alien plants. Methyl palmitate (MP) and methyl linolenate (ML) were used as exogenous allelopathic substances. The research investigated the differences of inhibitory effects of MP and ML on the growth of seedlings of Alternanthera philoxeroides, and calculated their morphological characteristics, biomass, physiological indicators and the response index (RI). The synthetical allelopathic index (SE) of 1 mmol/L MP was the smallest (- 0.26) and the allelopathic inhibition was the strongest; therefore, it was selected as a 13C-labeled allelochemical. The distribution of 1 mmol/L MP in different parts of A. philoxeroides and the correlation between the biomass ratios of roots, stems and leaves and the 13C content were studied by 13C stable isotope tracing experiments. Atom percent excess (APE) between roots, stems and leaves of A. philoxeroides treated with 1 mmol/L MP were significantly different in terms of magnitude, with leaves (0.17%) > roots (0.12%) > stems (0.07%). The root, stem and leaf biomass ratios of invasive weeds had great significant positive correlation with 13C content (p < 0.01, R2 between 0.96 and 0.99). This current research provides a new idea and method for the control of A. philoxeroides, but large-scale popularization remains to be studied.

scWizard: A web-based automated tool for classifying and annotating single cells and downstream analysis of single-cell RNA-seq data in cancers.

The emerging number of single-cell RNA-seq (scRNA-Seq) datasets allows the characterization of cell types across various cancer types. However, there is still lack of effective tools to integrate the various analysis of single-cells, especially for making fine annotation on subtype cells within the tumor microenvironment (TME). We developed scWizard, a point-and-click tool packaging automated process including our developed cell annotation method based on deep neural network learning and 11 downstream analyses methods. scWizard used 113,976 cells across 13 cancer types as a built-in reference dataset for training the hierarchical model enabling to automatedly classify and annotate 7 major cell types and 47 cell subtypes in the TME. scWizard provides a built-in pre-training set for user's flexible choice, and gives a higher accuracy for annotation subtypes of tumor-derived T-lymphocytes/natural killer cells (T/NK) and myeloid cells from different cancer types compared with the existing five methods. scWizard has good robustness in three independent cancer datasets, with an accuracy of 0.98 in annotating major cell types, 0.85 in annotating myeloid cell subtypes and 0.79 in annotating T/NK cell subtypes, indicting the wide applicability of scWizard in different cell types of cancers. Finally, the automatic analysis and visualization function of scWizard are presented by using the intrahepatic cholangiocarcinoma (ICC) scRNA-Seq dataset as a case. scWizard focuses on decoding TME and covers various analysis flows for cancer scRNA-Seq study, and provides an easy-to-use tool and a user-friendly interface for researchers widely, to further accelerate the biological discovery of cancer research.

Unsupervised Domain Adaptation for Medical Image Segmentation by Disentanglement Learning and Self-Training.

Unsupervised domain adaption (UDA), which aims to enhance the segmentation performance of deep models on unlabeled data, has recently drawn much attention. In this paper, we propose a novel UDA method (namely DLaST) for medical image segmentation via disentanglement learning and self-training. Disentanglement learning factorizes an image into domain-invariant anatomy and domain-specific modality components. To make the best of disentanglement learning, we propose a novel shape constraint to boost the adaptation performance. The self-training strategy further adaptively improves the segmentation performance of the model for the target domain through adversarial learning and pseudo label, which implicitly facilitates feature alignment in the anatomy space. Experimental results demonstrate that the proposed method outperforms the state-of-the-art UDA methods for medical image segmentation on three public datasets, i.e., a cardiac dataset, an abdominal dataset and a brain dataset. The code will be released soon.

Introduction of a Novel, Continuous, Noninvasive Estimation of Intracranial Pressure and Cerebral Perfusion Pressure Based on Tympanic Membrane Temperature.

OBJECTIVE: Hydrocephalus is a common but potentially life-threatening condition. However, valve malfunction makes further diagnosis difficult. Thus, we tried to develop a noninvasive method to detect the hydrocephalus intracranial pressure (ICP) during routine follow-up. METHODS: In group I, the patient was recruited because a spinal tap test was necessary for either disease diagnosis or treatment. In group II, patients were diagnosed with high ICP hydrocephalus and received shunt surgery. The tympanic membrane temperatures (TMTs) were recorded and plotted against the spinal tap pressure (STP) and shunt valve pressures. RESULTS: All patients in group I showed an above-normal STP (from 180 to 400 mm H2O). The STP presents with an inverted U-shaped curve when it is plotted against TMT (R2 = 0.9). When the STP was 286.1 mm H2O, the TMT approached its peak value, which was 38.61°C (101.5°F). However, when ICP was in the normal range (50-200 mm H2O), the TMT correlated with ICP in a linear regression model (R2 = 0.69; P < 0.001). In addition, the cerebral perfusion pressure (CPP) was calculated and plotted against TMT. The TMT-CPP was also shown as a parabola (R2 = 0.74). Based on the TMT-ICP algorithm, we invented a noninvasive ICP monitor system, which performs in a manner comparable to the Codman ICP Transducer (R2 = 0.9; P < 0.01). CONCLUSIONS: Both Y-Jiang TMT-ICP and TMT-CPP algorithms are useful to monitor the shunt outcomes and identify potential shunt failure. More importantly, these algorithms open the possibility for the rational acquisition of ICP and CPP noninvasively.

Chemical approaches for the preparation of ubiquitinated proteins via natural linkages.

Ubiquitination is an important posttranslation modification (PTM) that regulates a variety of cellular processes, including protein degradation, DNA repair, and viral infections. In this process, the C-terminal carboxyl group of ubiquitin (Ub) or poly-Ub is attached to the ε-amine of lysine (Lys) side chain of an acceptor protein through an isopeptide bond. Studying a molecular mechanism of ubiquitination and deubiquitination is fundamental for unraveling its precise role in health and disease and hence crucial for drug development. Enzymatic approaches for protein ubiquitination possess limited ability to selectivity install Ub or Ub chain on the desired position of an acceptor protein and often lead to heterogeneous mixtures. In the past decades, chemical protein (semi)synthesis has been proved to be an efficient tool to facilitate site-specific protein ubiquitination, which significantly contributes to decode the Ub signal at molecular and structural levels. In this review, we summarize the synthetic strategies developed for protein ubiquitination, and the achievements to generate monoubiquitinated, di-ubiquitinated, and tetraubiquitinated proteins with native isopeptide and ester bonds.

Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder.

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.

Outcomes of surgery for giant hepatic hemangioma.

BACKGROUND: The surgical indications for liver hemangioma remain unclear. METHODS: Data from 152 patients with hepatic hemangioma who underwent hepatectomy between 2004 and 2019 were retrospectively reviewed. We analyzed characteristics including tumor size, surgical parameters, and variables associated with Kasabach-Merritt syndrome and compared the outcomes of laparoscopic and open hepatectomy. Here, we describe surgical techniques for giant hepatic hemangioma and report on two meaningful cases. RESULTS: Most (63.8%) patients with hepatic hemangioma were asymptomatic. Most (86.4%) tumors from patients with Kasabach-Merritt syndrome were larger than 15 cm. Enucleation (30.9%), sectionectomy (28.9%), hemihepatectomy (25.7%), and the removal of more than half of the liver (14.5%) were performed through open (87.5%) and laparoscopic (12.5%) approaches. Laparoscopic hepatectomy is associated with an operative time, estimated blood loss, and major morbidity and mortality rate similar to those of open hepatectomy, but a shorter length of stay. 3D image reconstruction is an alternative for diagnosis and surgical planning for partial hepatectomy. CONCLUSION: The main indication for surgery is giant (> 10 cm) liver hemangioma, with or without symptoms. Laparoscopic hepatectomy was an effective option for hepatic hemangioma treatment. For extremely giant hemangiomas, 3D image reconstruction was indispensable. Hepatectomy should be performed by experienced hepatic surgeons.

Two novel online nomograms for predicting the survival of individual patients undergoing partial hepatectomy for huge hepatocellular carcinoma.

BACKGROUND: A method for predicting prognosis of patients who undergo partial hepatectomy for huge hepatocellular carcinoma (HHCC, diameter ≥10 cm) is currently lacking. This study aimed to establish two online nomograms to predict the overall survival (OS) and disease-free survival (DFS) for patients undergoing resection for HHCC. METHODS: The clinicopathologic characteristics and follow-up information of patients who underwent partial hepatectomy for HHCC at two medical centers were reviewed. Using a training cohort, a Cox model was used to identify the predictors of survival. Two dynamic nomograms for OS and DFS were developed and validated based on the data. RESULTS: Eight and nine independent factors derived from the multivariate analysis of the training cohort were screened and incorporated into the nomograms for OS and DFS, respectively. In the training cohort, the nomogram achieved concordance indices (C-indices) of 0.745 and 0.738 in predicting the OS and DFS, respectively. These results were supported by external validation (C-indices: 0.822 for OS and 0.827 for DFS). Further, the calibration curves of the endpoints showed a favorable agreement between the nomograms' assessments and actual observations. CONCLUSIONS: The two web-based nomograms demonstrated optimal predictive performance for patients undergoing partial hepatectomy for HHCC. This provides a practical method for a personalized prognosis based on an individual's underlying risk factors.

Systematic review of outcomes and meta-analysis of risk factors for prognosis after liver resection for hepatocellular carcinoma without cirrhosis.

Long-term overall survival (OS) after liver resection for non-cirrhotic hepatocellular carcinoma (NCHCC) has been reported recently. The aim of this study was to review outcomes systematically and analyze risk factors for survival after surgical resection for HCC without cirrhosis. A literature search was performed of the PubMed and Embase databases for papers published between January 1995 and October 2012, which focused on hepatic resection for HCC without underlying cirrhosis. Cochrane systematic review methodology was used for this review. Outcomes were OS, operative mortality and disease-free survival (DFS). Pooled hazard ratios (HR) were calculated using the random effects model for parameters considered as potential prognostic factors. Totally, 26 retrospective case series were eligible for inclusion. The 1-, 3- and 5-year OS rate after surgical resection of NCHCC ranged from 62% to 100%, 46.3%-78.0%, and 30%-64%, respectively. The corresponding DFS rates ranged from 48.7% to 84%, 31.0%-66.0%, and 24.0%-58.0%, respectively. Five variables were related to poor survival: multiple tumors (HR 1.68, 95%CI 1.25-2.11); larger tumor size (HR 2.66, 95%CI 1.69-3.63); non-clear resection margin (R0 resection) (HR 3.52, 95%CI 1.63-5.42); poor tumor stage (HR 2.61, 95%CI 1.64-3.58); and invasion of the lymphatic vessels (HR 4.85, 95%CI 2.67-7.02). In sum, hepatic resection provides excellent OS rates for patients with NCHCC, and results have tended to improve recently. Risk factors for poor prognosis comprise multiple tumors, lager tumor size, non-R0 resection and invasion of the lymphatic vessels.

Long Noncoding RNA WT1-AS Inhibit Cell Malignancy via miR-494-3p in Glioma.

Primary brain tumors are a rare occurrence in comparison to other malignancies, the most predominant form being glioma. Commonly, exposure to ionizing radiations and inheritance of associated conditions such a neurofibromatosis and tuberous sclerosis are the most common causes of development of glioma. However, understanding of the molecular mechanisms that drive glioma development is limited. We explore the role of aberration of microRNA namely miR-494-3p through long noncoding RNA WT1-AS in the development of gliomas. In this study, we found that, levels of WT1-AS were significantly reduced in glioma tissues and cell lines. The miR-494-3p levels were negatively correlated with WT1-AS levels. The cellular proliferation and invasiveness decreased in WT1-AS transfected cell lines. Further the half maximal inhibitory concentration (IC50) of chemotherapeutic agent temozolomide was significantly reduced in the presence of WT1-AS. The cotransfection of WT1-AS and miR-494-3p reduced activation of phospho-AKT (p-AKT). Expression of miR-494-3p is modulated by binding to long noncoding RNA WT1-AS. Deregulation of WT1-AS leads to aberrant expression of miR-494-3p leading to hyperactivation of AKT. This malformation may result in altering protective immune responses in malignancies. Targeting of WT1-AS, miR-494-3p, and AKT may be novel therapeutic options in treatment of glioma.

A Personalized Beat-to-Beat Heart Rate Detection System From Ballistocardiogram for Smart Home Applications.

This paper presents a ballistocardiogram (BCG) based beat-to-beat heart rate (HR) measurement system for healthcare applications in smart home. The BCG signal is picked up by two parallel connected piezoelectric elements, and processed by a robust HR detection algorithm. To deal with the diversity in BCG signal, a personalized heartbeat template is extracted to handle the diversity in BCG signal. The Pearson correlation coefficient is used to correlate between personalized template and the BCG signal. Heartbeats are detected by using the Hilbert Transform. The system is verified on 32 subjects with simultaneously recorded BCG and ECG signals. With the reference to ECG signal, we have achieved mean absolute relative error of the beat-to-beat intervals of 4.56%, and average absolute error ±standard deviation of absolute error for HR estimation in a 30-seconds window of 0.59 ±0.56 beats per minute. Meanwhile, 99.67% of the beat-to-beat intervals are detected on average.

Long-term outcomes after hepatectomy of huge hepatocellular carcinoma: A single-center experience in China.

BACKGROUND: Currently, hepatectomy remains the first-line therapy for hepatocellular carcinoma (HCC). However, surgery for patients with huge (>10 cm) HCCs is controversial. This retrospective study aimed to explore long-term survival after hepatectomy for patients with huge HCC. METHODS: The records of 188 patients with pathologically confirmed HCC who underwent curative hepatectomy between 2007 and 2017 were reviewed; patients were divided into three groups according to tumor size: huge (>10 cm; n = 84), large (5-10 cm; n = 51) and small (<5 cm; n = 53) HCC. Kaplan-Meier analysis was used to assess overall survival (OS) and disease-free survival (DFS), and log-rank analysis was performed for pairwise comparisons among the three groups. Risk factors for survival and recurrence were analyzed using the Cox proportional hazard model. RESULTS: The median follow-up period was 20 months. Although the prognosis of small HCC was better than that of huge and large HCC, OS and DFS were not significantly different between huge and large HCC (P = 0.099 and P = 0.831, respectively). A family history of HCC, poor Child-Pugh class, vascular invasion, diolame, pathologically positive margins, and operative time ≥240 min were identified as independent risk factors for OS and DFS in a multivariate model. Tumor size (>10 cm) had significant effect on OS, and postoperative antiviral therapy and postoperative complications also had significant effects on DFS. CONCLUSIONS: Huge HCC is not a contraindication of hepatectomy. Although most of these patients experienced recurrence after surgery, OS and DFS were not significantly different from those of patients with large HCC after resection.

Circular RNA ciRS-7 promotes the proliferation and metastasis of pancreatic cancer by regulating miR-7-mediated EGFR/STAT3 signaling pathway.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is the most deadly type of tumor, and its pathogenesis remains unknown. Circular RNAs (circRNAs) may be functional and bind to microRNAs and consequently, influence the activity of targeted mRNAs. Recent researches indicate that one circRNA, ciRS-7, acts as a sponge of miR-7 and thus, inhibits its activity. It is well known that miR-7 is a cancer suppressor in many cancers. However, the relationship between ciRS-7 and miR-7, and the role of ciRS-7 in PDAC, remains to be elucidated. METHODS: miR-7 and ciRS-7 expression in 41 pairs of PDAC tumors and their paracancerous tissues were detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The relationships between their expression levels and clinicopathological features in PDAC tissues were assessed. The relationship between miR-7 and ciRS-7 was also assessed by Spearman's correlation. We also used cell lines to evaluate the role of ciRS-7 in cell line behavior. The ciRS-7 interfere RNA (siRNA) and its empty vector were transfected into PDAC cells. PDAC cells proliferation and invasion abilities were detected by MTT assay and invasion analysis. The expression of proteins was assessed by Western blotting. RESULTS: ciRS-7 expression was significantly higher in PDAC tissues than paracancerous tissues (P = 0.002). However, miR-7 expression showed the opposite trend (P = 0.048). Moreover, ciRS-7 expression was inversely correlated with miR-7 in PDAC (rs = -0.353, P = 0.023). ciRS-7 expression was also significantly elevated in venous invasion (3.72 ± 2.93 vs. 2.14 ± 1.26; P = 0.028) and lymph node metastasis (4.19 ± 2.75 vs. 2.32 ± 1.90; P = 0.016) in PDAC patients. Furthermore, ciRS-7 knockdown suppressed cell proliferation and invasion of PDAC cells (P < 0.05), and the downregulation of ciRS-7 resulted in miR-7 overexpression and subsequent inhibition of epidermal growth factor receptor (EGFR) and signal transducer and activator of transcription 3 (STAT3). CONCLUSIONS: Circular RNA ciRS-7 plays an oncogene role in PDAC, partly by targeting miR-7 and regulating the EGFR/STAT3 signaling pathway.

Cross regulation of signaling pathways in gastrointestinal stromal tumor.

The Sonic hedgehog (Shh) signaling pathway may be interrelated with other signaling pathways, such as the phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) pathways in gastrointestinal stromal tumor (GIST). The present study investigated the interaction among Shh, PI3K and MAPK signaling pathways in GIST cells. The expression of PI3K, MAPK and Shh signaling pathways in GIST-H1 cells were upregulated by endothelial growth factor (EGF) and recombinant Shh (N-shh) stimulation, and were downregulated by specific inhibitors of each signaling pathway. The proliferation rate of GIST-H1 cells were significantly increased under EGF or N-shh treatment (P<0.01). In addition, this effect was partially prevented by the pretreatment of the inhibitors of these signaling pathways. In summary, a cross regulation exists among the Shh, PI3K and MAPK signaling pathways in GIST-H-1 cells. The combined use of the inhibitors of these signaling pathways is a potentially novel option for GIST targeted therapy.

Motion Artifact Removal for PPG Signals based on Accurate Fundamental Frequency Estimation and Notch Filtering.

This study proposes a new method for motion artifact (MA) removal in Photoplethysmography (PPG) signal that combines accurate heart rate (HR) frequency estimation and notch filtering. The method applies LMS-Newton adaptive filtering to reduce motion artifact noise and uses a novel HR correction stage for accurate HR frequency estimation. Notch filters are used to recover clean PPG signal from HR frequency and second harmonic frequency of PPG. On a widely used dataset of 12 recordings, our method achieves an averaged HR error of 0. 92bpm and a Pearson correlation of 0.997. Experimental results further show that our method can recover the PPG waveform with clear dicrotic peaks even for strongly MA-corrupted PPG signal.

Combining Adaptive Filter and Phase Vocoder for Heart Rate Monitoring Using Photoplethysmography During Physical Exercise.

This study presents a robust heart rate monitoring algorithm using photoplethysmography (PPG) signal during physical exercise. The proposed method combines two stage: motion artifact removal and frequency refinement. The cascaded normalized least mean square adaptive filter is used to attenuate the noise introduced by motion artifacts in the PPG signal. A phase vocoder technique is used to refine the frequency calculated by Fourier Transform, from which the heart rate is finally tracked. On a publicly available database of twelve PPG recordings, the proposed technique obtains an average absolute error (AAE) of 1.08 beat per minute (BPM). Person correlation coefficient of 0.997 is achieved between true heart rate and estimated heart rate. In contrast to other available approaches, the proposed method has merely one parameter to tune in spectral peak tracking step for heart rate estimation.