SNF2 Family Protein Fft3 Suppresses Nucleosome Turnover to Promote Epigenetic Inheritance and Proper Replication.

Heterochromatin can be epigenetically inherited in cis, leading to stable gene silencing. However, the mechanisms underlying heterochromatin inheritance remain unclear. Here, we identify Fft3, a fission yeast homolog of the mammalian SMARCAD1 SNF2 chromatin remodeler, as a factor uniquely required for heterochromatin inheritance, rather than for de novo assembly. Importantly, we find that Fft3 suppresses turnover of histones at heterochromatic loci to facilitate epigenetic transmission of heterochromatin in cycling cells. Moreover, Fft3 also precludes nucleosome turnover at several euchromatic loci to prevent R-loop formation, ensuring proper replication progression. Our analyses show that overexpression of Clr4/Suv39h, which is also required for efficient replication through these loci, suppresses phenotypes associated with the loss of Fft3. This work uncovers a conserved factor critical for epigenetic inheritance of heterochromatin and describes a mechanism in which suppression of nucleosome turnover prevents formation of structural barriers that impede replication at fragile regions in the genome.

The effects of multi-colour light filtering glasses on human brain wave activity.

The prevalence of electronic screens in modern society has significantly increased our exposure to high-energy blue and violet light wavelengths. Accumulating evidence links this exposure to adverse visual and cognitive effects and sleep disturbances. To mitigate these effects, the optical industry has introduced a variety of filtering glasses. However, the scientific validation of these glasses has often been based on subjective reports and a narrow range of objective measures, casting doubt on their true efficacy. In this study, we used electroencephalography (EEG) to record brain wave activity to evaluate the effects of glasses that filter multiple wavelengths (blue, violet, indigo, and green) on human brain activity. Our results demonstrate that wearing these multi-colour light filtering glasses significantly reduces beta wave power (13-30 Hz) compared to control or no glasses. Prior research has associated a reduction in beta power with the calming of heightened mental states, such as anxiety. As such, our results suggest that wearing glasses such as the ones used in this study may also positively change mental states, for instance, by promoting relaxation. This investigation is innovative in applying neuroimaging techniques to confirm that light-filtering glasses can induce measurable changes in brain activity.

Behavioral skill practice as a predictor of mood and family functioning in adolescents with bipolar and depressive mood disorders: Results of a 6-month randomized trial of family-focused therapy.

OBJECTIVE: Behavioral interventions require considerable practice of treatment skills in between therapy sessions. The effects of these treatments may vary with the degree to which patients are able to implement these practices. In offspring of parents with bipolar and major depressive disorders, we examined whether youth who frequently practiced communication and problem-solving skills between family-focused therapy (FFT) sessions had less severe mood symptoms and better psychosocial functioning over 6 months than youth who practiced less frequently. METHODS: We randomly assigned offspring (ages 12-19) of parents with mood disorders to 12 sessions of FFT plus a mobile app that encouraged the practice of communication, problem-solving and mood management skills (FFT-MyCoachConnect [MCC] condition) or 12 sessions of FFT with an app that only allowed for tracking of symptoms and stress (FFT-Track condition). Independent evaluators assessed youths' mood and psychosocial functioning at 9-week intervals over 27 weeks. Clinicians rated participants' between-session skill practice at each FFT session. RESULTS: FFT-MCC was associated with more frequent skill practice than FFT-Track over 18 weeks of treatment. Skill practice was associated with reductions in youths' mood instability and perceptions of family conflict over 27 weeks in both app conditions. Skill practice mediated the effects of app condition on youths' mood instability and family functioning. CONCLUSIONS: Mobile applications as adjuncts to family therapy for youth with mood disorders can help increase skill practice. These findings provide preliminary causal evidence for behavioral skill practice improving mood symptoms and family functioning among youth with mood disorders.

Use of Non-Destructive Ultrasonic Techniques as Characterization Tools for Different Varieties of Wine.

In this work, we have verified how non-destructive ultrasonic evaluation allows for acoustically characterizing different varieties of wine. For this, a 3.5 MHz transducer has been used by means of an immersion technique in pulse-echo mode. The tests were performed at various temperatures in the range 14-18 °C. The evaluation has been carried out studying, on the one hand, conventional analysis parameters (velocity and attenuation) and, on the other, less conventional parameters (frequency components). The experimental study comprised two stages. In the first, the feasibility of the study was checked by inspecting twelve samples belonging to six varieties of red and white wine. The results showed clearly higher ultrasonic propagation velocity values in the red wine samples. In the second, nine samples of different monovarietal wine varieties (Grenache, Tempranillo and Cabernet Sauvignon) were analyzed. The results show how ultrasonic velocity makes it possible to unequivocally classify the grape variety used in winemaking with the Cabernet Sauvignon variety having the highest values and the Grenache the lowest. In addition, the wines of the Tempranillo variety are those that present higher values of the attenuation coefficient, and those from the Grenache variety transmit higher frequency waves.

Exploration of cortical inhibition and habituation in insomnia: Based on CNV and EEG.

OBJECTIVE: Cognitive dysfunction with abnormal cortical inhibition and habituation has frequently been found in patients with insomnia. And the so-called contingent negative variation (CNV) and EEG power spectral density (FFT) may be the best choice to explore the underlining pathophysiology. METHODS: We used polysomnography (PSG) to record such objective PSG parameters. The amplitudes, latencies, areas of different CNV components such as oCNV, iCNV and tCNV, PINV have been selected and analyzed. Behavioral data such as manual reaction time (RT) has been analyzed. Spectral analysis was performed with fast Fourier transformation (FFT) on all channels to make a spectral analyses of EEG datas. RESULTS: The A-latency located in CZ or PZ were statistically longer in insomnia group than control group, the iCNV-latency located in insomnia group were statistically shorter than control group. The iCNV-amplitude located in insomnia group was lower than control group. The oCNV-amplitude or the tCNV-amplitude located in insomnia group was higher than control group. The oCNV-square, tCNV-square, or PINV-square located in insomnia group were significant larger than control group. ß1 or ß2 activity distributed in bilateral hemisphere were significantly increased in insomnia group than control group with different distributions. DISCUSSION: Our study revealed varied attentional and information processing in insomnia patients. Above all, we made a hypothesis with ceiling theory: Frontal lobe play an important role in maintaining cognitive processing, which needs much more energy consumption and leads to decreased fast EEG activity in frontal cortex, which contributes to reduced cortical inhibition, represented as abnormal CNV.

Compressed SVD-based L + S model to reconstruct undersampled dynamic MRI data using parallel architecture.

BACKGROUND: Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to capture the dynamic and functional information of body organs e.g. cardiac MRI is employed to assess cardiac structure and evaluate blood flow dynamics through the cardiac valves. Long scan time is the main drawback of MRI, which makes it difficult for the patients to remain still during the scanning process. OBJECTIVE: By collecting fewer measurements, MRI scan time can be shortened, but this undersampling causes aliasing artifacts in the reconstructed images. Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts. These algorithms are computationally expensive and require a long time for reconstruction which makes them infeasible for real-time clinical applications e.g. cardiac MRI. However, exploiting the inherent parallelism in these algorithms can help to reduce their computation time. METHODS: Low-rank plus sparse (L+S) matrix decomposition model is a technique used in literature to reconstruct the highly undersampled dynamic MRI (dMRI) data at the expense of long reconstruction time. In this paper, Compressed Singular Value Decomposition (cSVD) model is used in L+S decomposition model (instead of conventional SVD) to reduce the reconstruction time. The results provide improved quality of the reconstructed images. Furthermore, it has been observed that cSVD and other parts of the L+S model possess highly parallel operations; therefore, a customized GPU based parallel architecture of the modified L+S model has been presented to further reduce the reconstruction time. RESULTS: Four cardiac MRI datasets (three different cardiac perfusion acquired from different patients and one cardiac cine data), each with different acceleration factors of 2, 6 and 8 are used for experiments in this paper. Experimental results demonstrate that using the proposed parallel architecture for the reconstruction of cardiac perfusion data provides a speed-up factor up to 19.15× (with memory latency) and 70.55× (without memory latency) in comparison to the conventional CPU reconstruction with no compromise on image quality. CONCLUSION: The proposed method is well-suited for real-time clinical applications, offering a substantial reduction in reconstruction time.

Effect of scattered megavoltage x-rays on markerless tumor tracking using dual energy kilovoltage imaging.

PURPOSE: To determine the effect of megavoltage (MV) scatter on the accuracy of markerless tumor tracking (MTT) for lung tumors using dual energy (DE) imaging and to consider a post-processing technique to mitigate the effects of MV scatter on DE-MTT. METHODS: A Varian TrueBeam linac was used to acquire a series of interleaved 60/120 kVp images of a motion phantom with simulated tumors (10 and 15 mm diameter). Two sets of consecutive high/low energy projections were acquired, with and without MV beam delivery. The MV field sizes (FS) ranged from 2 × 2 cm2 -6 × 6 cm2 in steps of 1 × 1 cm2 . Weighted logarithmic subtraction was performed on sequential images to produce soft-tissue images for kV only (DEkV ) and kV with MV beam on (DEkV+MV ). Wavelet and fast Fourier transformation filtering (wavelet-FFT) was used to remove stripe noise introduced by MV scatter in the DE images ( DE kV + MV Corr ${\rm{DE}}_{{\rm{kV}} + {\rm{MV}}}^{{\rm{Corr}}}$ ). A template-based matching algorithm was then used to track the target on DEkV, DEkV+MV , and DE kV + MV Corr ${\rm{DE}}_{{\rm{kV}} + {\rm{MV}}}^{{\rm{Corr}}}$ images. Tracking accuracy was evaluated using the tracking success rate (TSR) and mean absolute error (MAE). RESULTS: For the 10 and 15 mm targets, the TSR for DEkV images was 98.7% and 100%, and MAE was 0.53 and 0.42 mm, respectively. For the 10 mm target, the TSR, including the effects of MV scatter, ranged from 86.5% (2 × 2 cm2 ) to 69.4% (6 × 6 cm2 ), while the MAE ranged from 2.05 mm to 4.04 mm. The application of wavelet-FFT algorithm to remove stripe noise ( DE kV + MV Corr ${\rm{DE}}_{{\rm{kV}} + {\rm{MV}}}^{{\rm{Corr}}}$ ) resulted in TSR values of 96.9% (2 × 2 cm2 ) to 93.4% (6 × 6 cm2 ) and subsequent MAE values were 0.89 mm to 1.37 mm. Similar trends were observed for the 15 mm target. CONCLUSION: MV scatter significantly impacts the tracking accuracy of lung tumors using DE images. Wavelet-FFT filtering can improve the accuracy of DE-MTT during treatment.

Turntable IMU Calibration Algorithm Based on the Fourier Transform Technique.

The paper suggests a new approach to calibration of a micromechanical inertial measurement unit. The data are collected on a simple rotating turntable with horizontal (or close to) rotation axis. For such a turntable, an electric screwdriver with fairly low rotation rate can be used. The algorithm is based on the Fourier transform applied to the rotation experimental data, implemented as FFT. The frequencies and amplitudes of the spectral peaks are calculated and collected in a small set of data, and calibration is done explicitly with these data. Calibration of an accelerometer triad and choosing the IMU coordinate frame are reduced to approximating the collected data with an ellipsoid in three dimensions. With rotation frequency calculated as the peak frequency of accelerometer readings, calibration of the gyros is a straightforward linear least square problem. The algorithm is purely algebraic, requires no iterations and no initial guess on the parameters, and thus encounters no convergence problems. The algorithm was tested both with simulated and experimental data, with some promising results.

Analysis of Interpolation Methods in the Validation of Backscattering Coefficient Products.

Validation is the basis of synthetic aperture radar (SAR) image quantification applications. Based on the point target of the field site, the radiation characteristics of the backscattering coefficient image can be used to optimize the SAR imaging, and the product production system can be more closely targeted, to ensure the image product accuracy in the actual quantification application. In this study, the validation of the backscattering coefficient image was examined using calibrators, and the radiometric properties of the image were evaluated by extracting the radar cross-section of each point target. Bilinear interpolation and fast Fourier transform (FFT) interpolation methods were introduced for the local area interpolation of point targets, and the two methods were compared from the perspective of response function imaging and validation accuracy. The results show that the FFT interpolation method is more favorable for validating the backscattering coefficient.

Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene.

Multitarget positioning technology, such as FMCW millimeter-wave radar, has broad application prospects in autonomous driving and related mobile scenarios. However, it is difficult for existing correlation algorithms to balance high resolution and low complexity, and it is also difficult to ensure the robustness of the positioning algorithm using an aging antenna. This paper proposes a super-resolution and low-complexity positioning algorithm based on the orthogonal matching pursuit algorithm that can achieve more accurate distance and angle estimation for multiple objects in a low-SNR environment. The algorithm proposed in this paper improves the resolving power by two and one orders of magnitude, respectively, compared to the classical FFT and MUSIC algorithms in the same signal-to-noise environment, and the complexity of the algorithm can be reduced by about 25-30%, with the same resolving power as the OMP algorithm. Based on the positioning algorithm proposed in our paper, we use the PSO algorithm to optimize the arrangement of an aging antenna array so that its angle estimation accuracy is equivalent to that observed when the antenna is intact, improving the positioning algorithm's robustness. This paper also further realizes the use of the proposed algorithm and a single-frame intermediate frequency signal to estimate the position angle information of the object and obtain its motion trajectory and velocity, verifying the proposed algorithm's estimation ability when it comes to these qualities in a moving scene. Furthermore, this paper designs and carries out simulations and experiments. The experimental results verify that the positioning algorithm proposed in this paper can achieve accuracy, robustness, and real-time performance in autonomous driving scenarios.

Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel.

This paper proposes a robust symbol timing synchronization scheme for return link initial access based on the Digital Video Broadcasting-Return Channel via Satellite 2nd generation (DVB-RCS2) system for the Low Earth Orbit (LEO) satellite channel. In most cases, the feedforward estimator structure is considered for implementing Time Division Multiple Access (TDMA) packet demodulators such as the DVB-RCS2 system. More specifically, the Non-Data-Aided (NDA) approach, without using any kind of preamble, pilot, and postamble symbols, is applicable for fine symbol timing synchronization. However, it hinders the improvement in estimation accuracy, especially when dealing with short packet lengths during the initial access from the User Terminal (UT) to the Gateway (GW). Moreover, when a UT sends a short random access packet for initial access or resource request to the LEO satellite channel, the conventional schemes suffer from a large Doppler error depending on UT's location in a beam and satellite velocity. To ameliorate these problems, we propose a novel symbol timing synchronization algorithm for GW, and its advantage is confirmed through computer simulation.

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory.

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems.

Since orthogonal frequency division multiplexing (OFDM) systems are very susceptible to symbol timing offset (STO) and carrier frequency offset (CFO), which cause inter-symbol interference (ISI) and inter-carrier interference (ICI), accurate STO and CFO estimations are very important. In this study, first, a new preamble structure based on the Zadoff-Chu (ZC) sequences was designed. On this basis, we proposed a new timing synchronization algorithm, called the continuous correlation peak detection (CCPD) algorithm, and its improved algorithm: the accumulated correlation peak detection (ACPD) algorithm. Next, the correlation peaks that were obtained during the timing synchronization were used for the frequency offset estimation. For this, the quadratic interpolation algorithm was adopted as the frequency offset estimation algorithm, which was better than the fast Fourier transform (FFT) algorithm. The simulation results showed that when the correct timing probability reached 100%, under the parameters of m = 8 and N = 512, the performance of the CCPD algorithm was 4 dB higher than that of Du's algorithm, and that of the ACPD algorithm was 7 dB. Under the same parameters, the quadratic interpolation algorithm also had a great performance improvement in both small and large frequency offsets, when compared with the FFT algorithm.

Invariant Pattern Recognition with Log-Polar Transform and Dual-Tree Complex Wavelet-Fourier Features.

In this paper, we propose a novel method for 2D pattern recognition by extracting features with the log-polar transform, the dual-tree complex wavelet transform (DTCWT), and the 2D fast Fourier transform (FFT2). Our new method is invariant to translation, rotation, and scaling of the input 2D pattern images in a multiresolution way, which is very important for invariant pattern recognition. We know that very low-resolution sub-bands lose important features in the pattern images, and very high-resolution sub-bands contain significant amounts of noise. Therefore, intermediate-resolution sub-bands are good for invariant pattern recognition. Experiments on one printed Chinese character dataset and one 2D aircraft dataset show that our new method is better than two existing methods for a combination of rotation angles, scaling factors, and different noise levels in the input pattern images in most testing cases.

Modal Decomposition of Acoustic Emissions from Pencil-Lead Breaks in an Isotropic Thin Plate.

Acoustic emission (AE) testing and Lamb wave inspection techniques have been widely used in non-destructive testing and structural health monitoring. For thin plates, the AEs arising from structural defect development (e.g., fatigue crack propagation) propagate as Lamb waves, and Lamb wave modes can be used to provide important information about the growth and localisation of defects. However, few sensors can be used to achieve the in situ wavenumber-frequency modal decomposition of AEs. This study explores the ability of a new multi-element piezoelectric sensor array to decompose AEs excited by pencil lead breaks (PLBs) on a thin isotropic plate. In this study, AEs were generated by out-of-plane (transverse) and in-plane (longitudinal) PLBs applied at the edge of the plate, and waveforms were recorded by both the new sensor array and a commercial AE sensor. Finite element analysis (FEA) simulations of PLBs were also conducted and the results were compared with the experimental results. To identify the wave modes present, the longitudinal and transverse PLB test results recorded by the new sensor array at five different plate locations were compared with FEA simulations using the same arrangement. Two-dimensional fast Fourier Transforms were then applied to the AE wavefields. It was found that the AE modal composition was dependent on the orientation of the PLB direction. The results suggest that this new sensor array can be used to identify the AE wave modes excited by PLBs in both in-plane and out-of-plane directions.

Fast Fourier Transform-weighted Photoacoustic Imaging by In Vivo Magnetic Alignment of Hybrid Nanorods.

Photoacoustic (PA) imaging uses photon-phonon conversion for high-resolution tomography of biological tissues and functions. Exogenous contrast agents are often added to improve the image quality, but the interference from endogenous molecules diminishes the imaging sensitivity and specificity. We report a background-free PA imaging technique based on the active modulation of PA signals via magnetic alignment of Fe3O4@Au hybrid nanorods. Switching the field direction creates enhanced and deactivated PA imaging modalities, enabling a simple pixel subtraction to effectively minimize background noises. Under an alternating magnetic field, the nanorods exhibit PA signals of coherently periodic changes that can be converted into a sharp peak in a frequency domain via the fast Fourier transform. Automatic pixel-wise screening of nanorod signals performed using a computational algorithm across a time-sequence set of PA images regenerates a background-free PA image with significantly improved contrast, specificity, and fidelity.

[Evidence-based inpatient psychotherapy of bipolar disorders]. / Evidenzbasierte stationäre Psychotherapie bipolarer Störungen.

BACKGROUND: Although psychotherapy is an important pillar in the treatment of bipolar disorders, alongside pharmacotherapy, non-drug and complementary procedures, there is no up to date evidence synthesis for inpatient psychotherapeutic treatment and work with caregivers. OBJECTIVE: To review and evaluate the current study situation on evidence-based inpatient psychotherapy for bipolar disorders. MATERIAL AND METHODS: 1.Summary of the evidence for inpatient psychotherapy in adolescents and adults with bipolar disorders from current review articles and guidelines (German S3 guidelines, Australian, Canadian, and British NICE guidelines). 2. Systematic literature search (PRISMA) in Cochrane trials and Medline (via PubMed). 2a. Identification of original articles using the following search term: "bipolar fft" OR "bipolar ipsrt" OR "bipolar cbt" OR "bipolar cognitive remediation" OR "bipolar psychotherapy inpatient". 2b. Screening of n = 942 publications on the following inclusion criteria: randomized controlled efficacy trials, inpatient treatment/recruitment in the inpatient setting, adolescent or adult patients with bipolar disorder or caregivers. RESULTS: The guidelines recommend a combination of pharmacotherapy and psychotherapy for the treatment of patients with bipolar disorders (so far no evidence-based presentation of inpatient psychotherapy). The results from reviews and original papers are heterogeneous. Recently described evidence-based psychotherapeutic approaches for inpatient treatment are family focused therapy (FFT), interpersonal and social rhythm therapy (IPSRT) and psychoeducation. CONCLUSION: Although the current evidence is heterogeneous and further systematic studies are necessary, the results indicate that psychotherapy should be started or initiated in the inpatient setting with inclusion of caregivers.

Text as signal. A tutorial with case studies focusing on social media (Twitter).

Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.

An Efficient CS-Based Spectral Peak Search Method.

Spectral peak search is an essential part of the frequency domain parametric method. In this paper, a spectral peak search algorithm employing the principle of compressed sensing (CS) is proposed to rapidly estimate the spectral peaks. The algorithm adopts fast Fourier transform (FFT) with a few points to obtain the coarsely estimated spectral peak positions, and then only three small-scale inner products are iteratively calculated by increasing the input sequence length to rapidly refine the estimated positions. Compared with the conventional methods, this algorithm can directly capture the exact locations of spectral peaks without acquiring the entire spectrum. In addition, the proposed algorithm can be easily integrated into the existing frequency domain interpolation methods to accurately determine the spectral peak positions, and if so, only 30% of inner product operations of the original algorithms are required. Theoretical analysis and numerical results show that this algorithm yields accurate results with low complexity for analyzing both one-dimensional and two-dimensional signals.

Noise in ICUs: Review and Detailed Analysis of Long-Term SPL Monitoring in ICUs in Northern Spain.

Intensive care units (ICUs) are busy and noisy areas where patients and professional staff can be exposed to acoustic noise for long periods of time. In many cases, noise levels significantly exceed the levels recommended by the official health organisations. This situation can affect not only patient recovery but also professional staff, making ICUs unhealthy work and treatment environments. To introduce the measures and reduce the acoustic noise in the ICU, acoustic noise levels should first be measured and then appropriately analysed. However, in most studies dealing with this problem, measurements have been performed manually over short periods, leading to limited data being collected. They are usually followed by insufficient analysis, which in turn results in inadequate measures and noise reduction. This paper reviews recent works dealing with the problem of excessively high noise levels in ICUs and proposes a more thorough analysis of measured data both in the time and frequency domains. Applied frequency domain analysis identifies the cyclic behaviour of the measured sound pressure levels (SPLs) and detects the dominant frequency components in the SPL time series. Moreover, statistical analyses are produced to depict the patterns and SPLs to which patients in ICUs are typically exposed during their stay in the ICU. It has been shown that the acoustic environment is very similar every night, while it can vary significantly during the day or evening periods. However, during most of the observed time, recorded SPLs were significantly above the prescribed values, indicating an urgent need for their control and reduction. To effectively tackle this problem, more detailed information about the nature of noise during each of the analysed periods of the day is needed. This issue will be addressed in the continuation of this project.